Initial processing

This document describes the analysis of TCR repertoires for the manuscript “Unique roles of coreceptor-bound LCK in helper and cytotoxic T cells” by Horkova et al. The analysis workflow included:

  • sorting of CD4 and CD8 cells from lymph nodes and thymi of LckWT, LckCA and LckCA/KR mice,
  • isolation of RNA using RNA Clean & Concentrator-5 kit
  • preparation of TCR-enriched libraries using the NEBNext® Mouse Immune Sequencing Kit
  • sequencing the libraries on Illumina Miseq, 300bp paired-end reads
  • demultiplexing reads in Illumina BaseSpace
  • processing fastq files using the recommended pRESTO workflow on Galaxy with default parameters
  • aligning processed fastq files using MiXCR

Raw data are deposited in the Sequence Read Archive (PRJNA872031). Processed files can be downloaded download from Zenodo. The Zenodo archive contains the following files, which are needed to run this script:

  • merged outputs from MiXCR merged_TCR_repertoires.csv
  • metadata file metadata_Lck.csv
  • count tables with raw counts of CDR3 TRA and TRB sequences in each sample count_table_tra.csv, "count_table_trb.csv"
  • TRA and TRB repertoires prepared for processing with the Immunarch package immdata_tra.rds, immdata_trb.rds

Let’s read the required files:

#
merged_repertoires <- read.csv("merged_TCR_repertoires.csv")
md <- read.csv("metadata_Lck.csv")

Count tables

We will start with processing of the file merged_TCR_repertoires.csv. This file contains outputs of MiXCR software, i.e., all the assembled CDR3 clonotypes, their counts in each sample, nucleotide and amino-acid sequences, all V, D and J alignments with scores, together with some meta-data. the column num_id contains info about the sample number, which we will frequently use to attach meta-data to sample numbers.

Here, we summarize the variables in the file merged_TCR_repertoires.csv:

kable(ExpData(data=merged_repertoires, type=2)) %>%
  kable_styling(full_width = F, font_size = 11, 
                bootstrap_options = c("striped", "hover", "condensed", "responsive"))
Index Variable_Name Variable_Type Sample_n Missing_Count Per_of_Missing No_of_distinct_values
1 cloneId integer 1001983 0 0.000 68138
2 cloneCount integer 1001983 0 0.000 120
3 cloneFraction numeric 1001983 0 0.000 994
4 targetSequences character 1001983 0 0.000 823904
5 targetQualities character 1001983 0 0.000 746125
6 allVHitsWithScore character 1001983 0 0.000 93452
7 allDHitsWithScore character 566670 435313 0.434 327
8 allJHitsWithScore character 1001983 0 0.000 6929
9 allCHitsWithScore character 955 1001028 0.999 122
10 allVAlignments character 1001983 0 0.000 14932
11 allDAlignments character 566670 435313 0.434 13102
12 allJAlignments character 1001983 0 0.000 17988
13 allCAlignments character 144 1001839 1.000 1
14 nSeqCDR3 character 1001983 0 0.000 823904
15 minQualCDR3 integer 1001983 0 0.000 70
16 nSeqFR4 logical 0 1001983 1.000 0
17 aaSeqCDR3 character 1001983 0 0.000 541554
18 num_id integer 1001983 0 0.000 28
19 chain character 1001983 0 0.000 2
20 Mouse_strain character 1001983 0 0.000 3
21 Cell_type character 1001983 0 0.000 2
22 Organ character 1001983 0 0.000 2
23 Cell_count numeric 1001983 0 0.000 28
24 Exp integer 1001983 0 0.000 3
25 INDEX_i7 integer 1001983 0 0.000 12
26 INDEX_i5 integer 1001983 0 0.000 3

First, we will generate summary count tables for TRA and TRB CDR3 sequences (these tables are provided as Supplementary table S1 in the manuscript). We will reorder the file so that there will be one column for each sample, and we will change the num_id of the samples to meaningful names.

count_table_tra <- merged_repertoires %>% 
  dplyr::filter(chain == "TRA") %>% 
  dplyr::select(aaSeqCDR3, num_id, cloneCount)

counts_tra <- count_table_tra %>% 
  pivot_wider(names_from = num_id, values_from = cloneCount, values_fill = 0, values_fn = sum)

md2_tra <- merged_repertoires %>% filter(chain == "TRA") %>% group_by(aaSeqCDR3) %>% slice_head(n = 1)

excel_count_table_tra <- counts_tra %>% left_join(md2_tra %>% select(allVHitsWithScore, allDHitsWithScore, allJHitsWithScore))

md_to_join <- md %>% mutate(new_name = paste(Cell_type, Organ, Mouse_strain, paste0("Exp0",Exp)))  %>% select(num_id, new_name) %>% arrange(num_id)

order <- match(as.numeric(colnames(counts_tra)[2:29]), md_to_join$num_id)

colnames(excel_count_table_tra)[2:29] <- pull(md_to_join, new_name)[order]
excel_count_table_tra2 <- excel_count_table_tra %>% dplyr::select(1, 31, 32, 30, 22, 12, 25, 11, 6, 26, 13, 18, 4, 28, 17, 14, 8, 23, 9, 7, 16, 24, 2, 21, 5, 29, 10, 20, 19, 3, 15, 27)

Here, we show the resulting table for TRA:

kable(excel_count_table_tra2 %>% head) %>%
  kable_styling(full_width = F, font_size = 11, 
                bootstrap_options = c("striped", "hover", "condensed", "responsive"))
aaSeqCDR3 allDHitsWithScore allJHitsWithScore allVHitsWithScore CD4 Lymph nodes CA Exp01 CD4 Lymph nodes CA Exp02 CD4 Lymph nodes CA Exp03 CD4 Lymph nodes CAKR Exp02 CD4 Lymph nodes CAKR Exp03 CD4 Lymph nodes WT Exp02 CD4 Lymph nodes WT Exp03 CD4 Thymus CA Exp01 CD4 Thymus CA Exp03 CD4 Thymus CAKR Exp01 CD4 Thymus CAKR Exp02 CD4 Thymus CAKR Exp03 CD4 Thymus WT Exp02 CD4 Thymus WT Exp03 CD8 Lymph nodes CA Exp02 CD8 Lymph nodes CA Exp03 CD8 Lymph nodes CAKR Exp02 CD8 Lymph nodes CAKR Exp03 CD8 Lymph nodes WT Exp01 CD8 Lymph nodes WT Exp02 CD8 Lymph nodes WT Exp03 CD8 Thymus CA Exp01 CD8 Thymus CA Exp03 CD8 Thymus CAKR Exp02 CD8 Thymus CAKR Exp03 CD8 Thymus WT Exp01 CD8 Thymus WT Exp02 CD8 Thymus WT Exp03
CAASASSGSWQLIF NA TRAJ22*00(303.6) TRAV14N-200(1026.1),TRAV14D-200(1017.8),TRAV14D-100(973.4),TRAV14N-100(973.4),TRAV14-2*00(944.4) 38 24 73 30 43 44 63 11 30 5 8 26 41 43 104 116 67 169 131 233 160 17 93 21 35 84 14 139
CAASNMGYKLTF NA TRAJ9*00(284.4) TRAV7-200(731.5),TRAV7D-200(731.5) 16 11 21 15 14 10 21 5 18 5 0 1 22 9 19 25 16 39 34 38 35 4 32 2 8 20 4 32
CAVSASSGSWQLIF NA TRAJ22*00(304.2) TRAV3D-300(975.2),TRAV3N-300(975.2),TRAV3-3*00(944.1) 10 5 6 5 9 3 5 2 5 0 5 4 8 6 23 23 7 35 26 37 32 4 11 4 5 19 4 28
CAASDNYAQGLTF NA TRAJ26*00(284) TRAV14D-100(878.2),TRAV14N-100(878.2),TRAV14D-200(848.2),TRAV14N-200(848.2) 4 0 6 8 3 2 3 0 5 2 0 0 5 6 10 12 6 17 22 23 21 3 6 3 6 15 6 26
CALSDRYNQGKLIF NA TRAJ23*00(272.3) TRAV12D-300(1377.3),TRAV12N-300(1377.3) 1 1 8 0 1 1 1 0 0 0 0 1 0 1 12 11 14 27 50 59 40 1 7 1 3 16 5 21
CAASDDTNAYKVIF NA TRAJ30*00(295.7) TRAV14-1*00(966.7) 0 0 1 4 2 0 3 1 0 0 1 0 6 3 13 10 5 7 18 17 13 2 12 4 9 7 1 23
count_table_trb <- merged_repertoires %>% 
  dplyr::filter(chain == "TRB") %>% 
  dplyr::select(aaSeqCDR3, num_id, cloneCount)

counts_trb <- count_table_trb %>% 
  pivot_wider(names_from = num_id, values_from = cloneCount, values_fill = 0, values_fn = sum)

md2_trb <- merged_repertoires %>% 
  filter(chain == "TRB") %>% 
  group_by(aaSeqCDR3) %>% 
  slice_head(n = 1)

excel_count_table_trb <- counts_trb %>% 
  left_join(md2_trb %>% 
              select(allVHitsWithScore, allDHitsWithScore, allJHitsWithScore))

md_to_join <- md %>% 
  mutate(new_name = paste(Cell_type, Organ, Mouse_strain, paste0("Exp0",Exp))) %>% 
  select(num_id, new_name) %>% 
  arrange(num_id)

order <- match(as.numeric(colnames(counts_trb)[2:29]), md_to_join$num_id)

colnames(excel_count_table_trb)[2:29] <- pull(md_to_join, new_name)[order]

excel_count_table_trb2 <- excel_count_table_trb %>% 
  dplyr::select(1, 30:32, 22, 12, 25, 11, 6, 26, 13, 18, 4, 28, 17, 14, 8, 23, 9, 7, 16, 24, 2, 21, 5, 29, 10, 20, 19, 3, 15, 27)

Here, we show the resulting table for TRB:

kable(excel_count_table_trb2 %>% head) %>%
  kable_styling(full_width = F, font_size = 11, 
                bootstrap_options = c("striped", "hover", "condensed", "responsive"))
aaSeqCDR3 allVHitsWithScore allDHitsWithScore allJHitsWithScore CD4 Lymph nodes CA Exp01 CD4 Lymph nodes CA Exp02 CD4 Lymph nodes CA Exp03 CD4 Lymph nodes CAKR Exp02 CD4 Lymph nodes CAKR Exp03 CD4 Lymph nodes WT Exp02 CD4 Lymph nodes WT Exp03 CD4 Thymus CA Exp01 CD4 Thymus CA Exp03 CD4 Thymus CAKR Exp01 CD4 Thymus CAKR Exp02 CD4 Thymus CAKR Exp03 CD4 Thymus WT Exp02 CD4 Thymus WT Exp03 CD8 Lymph nodes CA Exp02 CD8 Lymph nodes CA Exp03 CD8 Lymph nodes CAKR Exp02 CD8 Lymph nodes CAKR Exp03 CD8 Lymph nodes WT Exp01 CD8 Lymph nodes WT Exp02 CD8 Lymph nodes WT Exp03 CD8 Thymus CA Exp01 CD8 Thymus CA Exp03 CD8 Thymus CAKR Exp02 CD8 Thymus CAKR Exp03 CD8 Thymus WT Exp01 CD8 Thymus WT Exp02 CD8 Thymus WT Exp03
CASSDSAETLYF TRBV13-300(755.1),TRBV13-100(664.5) NA TRBJ2-3*00(245) 7 1 6 1 12 1 10 6 3 2 0 0 2 8 13 14 11 14 23 23 10 4 6 0 0 6 5 11
CASSDAEQFF TRBV13-1*00(819.6) NA TRBJ2-1*00(219.2) 0 0 5 0 1 2 1 2 2 0 0 0 1 1 9 7 5 4 12 9 8 3 3 3 4 11 3 4
CASSDAGYEQYF TRBV13-1*00(1283.8) NA TRBJ2-7*00(215) 0 1 2 0 3 10 1 9 5 1 0 1 1 0 11 10 13 14 24 41 22 4 6 0 3 14 4 13
CASSDWGGYAEQFF TRBV13-1*00(676) TRBD2*00(45) TRBJ2-1*00(235) 3 1 1 4 0 0 0 4 1 0 0 0 1 1 4 10 34 23 14 18 9 2 1 4 4 9 3 3
CASSDSGGQDTQYF TRBV13-3*00(886.3) TRBD2*00(35) TRBJ2-5*00(235) 1 0 2 1 0 0 0 0 0 0 0 0 1 0 2 2 1 4 14 3 0 0 1 0 2 2 0 1
CASRDRNTEVFF TRBV13-3*00(979) TRBD1*00(30) TRBJ1-1*00(232.7) 6 1 3 3 0 0 3 0 2 0 0 7 0 1 8 2 5 7 9 3 6 0 4 0 0 4 1 5

Prop tables and less than 5

Prop table TRA

excel_count_table_tra3 <- excel_count_table_tra2 %>% 
  mutate(nkt_trav11_traj18 = if_else(
    (grepl(allVHitsWithScore, pattern = "TRAV11") & 
       (grepl(allJHitsWithScore, pattern = "TRAJ18"))),"yes","no"))

excel_count_table_tra4 <- excel_count_table_tra3 %>% 
  filter(nkt_trav11_traj18 == "no")

count_table_tra4 <- as.matrix(excel_count_table_tra4[,5:32])
rownames(count_table_tra4) <- excel_count_table_tra4$aaSeqCDR3

tra4_norm <- scale(count_table_tra4, center=FALSE, scale=colSums(count_table_tra4))

prop.table.tra <- cbind(tra4_norm, 
                        excel_count_table_tra4 %>% 
                          select(-starts_with("CD")) ) 

prop.table.tra2 <- prop.table.tra %>% 
  dplyr::select(29, 31, 30, 32, 1:28)

prop.table.tra2 %>% head
##                     aaSeqCDR3 allJHitsWithScore allDHitsWithScore
## CAASASSGSWQLIF CAASASSGSWQLIF  TRAJ22*00(303.6)              <NA>
## CAASNMGYKLTF     CAASNMGYKLTF   TRAJ9*00(284.4)              <NA>
## CAVSASSGSWQLIF CAVSASSGSWQLIF  TRAJ22*00(304.2)              <NA>
## CAASDNYAQGLTF   CAASDNYAQGLTF    TRAJ26*00(284)              <NA>
## CALSDRYNQGKLIF CALSDRYNQGKLIF  TRAJ23*00(272.3)              <NA>
## CAASDDTNAYKVIF CAASDDTNAYKVIF  TRAJ30*00(295.7)              <NA>
##                                                                                                   allVHitsWithScore
## CAASASSGSWQLIF TRAV14N-2*00(1026.1),TRAV14D-2*00(1017.8),TRAV14D-1*00(973.4),TRAV14N-1*00(973.4),TRAV14-2*00(944.4)
## CAASNMGYKLTF                                                                   TRAV7-2*00(731.5),TRAV7D-2*00(731.5)
## CAVSASSGSWQLIF                                              TRAV3D-3*00(975.2),TRAV3N-3*00(975.2),TRAV3-3*00(944.1)
## CAASDNYAQGLTF                       TRAV14D-1*00(878.2),TRAV14N-1*00(878.2),TRAV14D-2*00(848.2),TRAV14N-2*00(848.2)
## CALSDRYNQGKLIF                                                            TRAV12D-3*00(1377.3),TRAV12N-3*00(1377.3)
## CAASDDTNAYKVIF                                                                                   TRAV14-1*00(966.7)
##                CD4 Lymph nodes CA Exp01 CD4 Lymph nodes CA Exp02
## CAASASSGSWQLIF             2.727925e-03             0.0035273369
## CAASNMGYKLTF               1.148600e-03             0.0016166961
## CAVSASSGSWQLIF             7.178751e-04             0.0007348618
## CAASDNYAQGLTF              2.871500e-04             0.0000000000
## CALSDRYNQGKLIF             7.178751e-05             0.0001469724
## CAASDDTNAYKVIF             0.000000e+00             0.0000000000
##                CD4 Lymph nodes CA Exp03 CD4 Lymph nodes CAKR Exp02
## CAASASSGSWQLIF             3.841094e-03               0.0027958993
## CAASNMGYKLTF               1.104972e-03               0.0013979497
## CAVSASSGSWQLIF             3.157064e-04               0.0004659832
## CAASDNYAQGLTF              3.157064e-04               0.0007455732
## CALSDRYNQGKLIF             4.209419e-04               0.0000000000
## CAASDDTNAYKVIF             5.261773e-05               0.0003727866
##                CD4 Lymph nodes CAKR Exp03 CD4 Lymph nodes WT Exp02
## CAASASSGSWQLIF               2.668321e-03             0.0035501049
## CAASNMGYKLTF                 8.687558e-04             0.0008068420
## CAVSASSGSWQLIF               5.584859e-04             0.0002420526
## CAASDNYAQGLTF                1.861620e-04             0.0001613684
## CALSDRYNQGKLIF               6.205399e-05             0.0000806842
## CAASDDTNAYKVIF               1.241080e-04             0.0000000000
##                CD4 Lymph nodes WT Exp03 CD4 Thymus CA Exp01 CD4 Thymus CA Exp03
## CAASASSGSWQLIF             3.291020e-03        0.0028328612        0.0022079929
## CAASNMGYKLTF               1.097007e-03        0.0012876642        0.0013247958
## CAVSASSGSWQLIF             2.611921e-04        0.0005150657        0.0003679988
## CAASDNYAQGLTF              1.567152e-04        0.0000000000        0.0003679988
## CALSDRYNQGKLIF             5.223842e-05        0.0000000000        0.0000000000
## CAASDDTNAYKVIF             1.567152e-04        0.0002575328        0.0000000000
##                CD4 Thymus CAKR Exp01 CD4 Thymus CAKR Exp02
## CAASASSGSWQLIF          0.0013199578          0.0037400655
## CAASNMGYKLTF            0.0013199578          0.0000000000
## CAVSASSGSWQLIF          0.0000000000          0.0023375409
## CAASDNYAQGLTF           0.0005279831          0.0000000000
## CALSDRYNQGKLIF          0.0000000000          0.0000000000
## CAASDDTNAYKVIF          0.0000000000          0.0004675082
##                CD4 Thymus CAKR Exp03 CD4 Thymus WT Exp02 CD4 Thymus WT Exp03
## CAASASSGSWQLIF          0.0033652602        0.0025633010        2.775089e-03
## CAASNMGYKLTF            0.0001294331        0.0013754298        5.808325e-04
## CAVSASSGSWQLIF          0.0005177323        0.0005001563        3.872217e-04
## CAASDNYAQGLTF           0.0000000000        0.0003125977        3.872217e-04
## CALSDRYNQGKLIF          0.0001294331        0.0000000000        6.453695e-05
## CAASDDTNAYKVIF          0.0000000000        0.0003751172        1.936108e-04
##                CD8 Lymph nodes CA Exp02 CD8 Lymph nodes CA Exp03
## CAASASSGSWQLIF             0.0099369387             0.0093881515
## CAASNMGYKLTF               0.0018154023             0.0020233085
## CAVSASSGSWQLIF             0.0021975922             0.0018614438
## CAASDNYAQGLTF              0.0009554749             0.0009711881
## CALSDRYNQGKLIF             0.0011465698             0.0008902557
## CAASDDTNAYKVIF             0.0012421173             0.0008093234
##                CD8 Lymph nodes CAKR Exp02 CD8 Lymph nodes CAKR Exp03
## CAASASSGSWQLIF               0.0080917874               0.0096842588
## CAASNMGYKLTF                 0.0019323671               0.0022348289
## CAVSASSGSWQLIF               0.0008454106               0.0020056157
## CAASDNYAQGLTF                0.0007246377               0.0009741562
## CALSDRYNQGKLIF               0.0016908213               0.0015471893
## CAASDDTNAYKVIF               0.0006038647               0.0004011231
##                CD8 Lymph nodes WT Exp01 CD8 Lymph nodes WT Exp02
## CAASASSGSWQLIF              0.009602698              0.011932808
## CAASNMGYKLTF                0.002492303              0.001946123
## CAVSASSGSWQLIF              0.001905879              0.001894909
## CAASDNYAQGLTF               0.001612667              0.001177917
## CALSDRYNQGKLIF              0.003665152              0.003021612
## CAASDDTNAYKVIF              0.001319455              0.000870634
##                CD8 Lymph nodes WT Exp03 CD8 Thymus CA Exp01 CD8 Thymus CA Exp03
## CAASASSGSWQLIF             0.0097454014        0.0062111801        0.0073130455
## CAASNMGYKLTF               0.0021318066        0.0014614541        0.0025163167
## CAVSASSGSWQLIF             0.0019490803        0.0014614541        0.0008649839
## CAASDNYAQGLTF              0.0012790839        0.0010960906        0.0004718094
## CALSDRYNQGKLIF             0.0024363503        0.0003653635        0.0005504443
## CAASDDTNAYKVIF             0.0007918139        0.0007307271        0.0009436188
##                CD8 Thymus CAKR Exp02 CD8 Thymus CAKR Exp03 CD8 Thymus WT Exp01
## CAASASSGSWQLIF          0.0084643289          0.0065963061        0.0088170463
## CAASNMGYKLTF            0.0008061266          0.0015077271        0.0020992967
## CAVSASSGSWQLIF          0.0016122531          0.0009423294        0.0019943319
## CAASDNYAQGLTF           0.0012091898          0.0011307953        0.0015744726
## CALSDRYNQGKLIF          0.0004030633          0.0005653977        0.0016794374
## CAASDDTNAYKVIF          0.0016122531          0.0016961930        0.0007347539
##                CD8 Thymus WT Exp02 CD8 Thymus WT Exp03
## CAASASSGSWQLIF        0.0053846154         0.007650815
## CAASNMGYKLTF          0.0015384615         0.001761339
## CAVSASSGSWQLIF        0.0015384615         0.001541171
## CAASDNYAQGLTF         0.0023076923         0.001431088
## CALSDRYNQGKLIF        0.0019230769         0.001155878
## CAASDDTNAYKVIF        0.0003846154         0.001265962
ExpData(data=prop.table.tra2, type=2)
##    Index              Variable_Name Variable_Type Sample_n Missing_Count
## 1      1                  aaSeqCDR3     character   114630             0
## 2      2          allJHitsWithScore     character   114630             0
## 3      3          allDHitsWithScore     character     4769        109861
## 4      4          allVHitsWithScore     character   114630             0
## 5      5   CD4 Lymph nodes CA Exp01       numeric   114630             0
## 6      6   CD4 Lymph nodes CA Exp02       numeric   114630             0
## 7      7   CD4 Lymph nodes CA Exp03       numeric   114630             0
## 8      8 CD4 Lymph nodes CAKR Exp02       numeric   114630             0
## 9      9 CD4 Lymph nodes CAKR Exp03       numeric   114630             0
## 10    10   CD4 Lymph nodes WT Exp02       numeric   114630             0
## 11    11   CD4 Lymph nodes WT Exp03       numeric   114630             0
## 12    12        CD4 Thymus CA Exp01       numeric   114630             0
## 13    13        CD4 Thymus CA Exp03       numeric   114630             0
## 14    14      CD4 Thymus CAKR Exp01       numeric   114630             0
## 15    15      CD4 Thymus CAKR Exp02       numeric   114630             0
## 16    16      CD4 Thymus CAKR Exp03       numeric   114630             0
## 17    17        CD4 Thymus WT Exp02       numeric   114630             0
## 18    18        CD4 Thymus WT Exp03       numeric   114630             0
## 19    19   CD8 Lymph nodes CA Exp02       numeric   114630             0
## 20    20   CD8 Lymph nodes CA Exp03       numeric   114630             0
## 21    21 CD8 Lymph nodes CAKR Exp02       numeric   114630             0
## 22    22 CD8 Lymph nodes CAKR Exp03       numeric   114630             0
## 23    23   CD8 Lymph nodes WT Exp01       numeric   114630             0
## 24    24   CD8 Lymph nodes WT Exp02       numeric   114630             0
## 25    25   CD8 Lymph nodes WT Exp03       numeric   114630             0
## 26    26        CD8 Thymus CA Exp01       numeric   114630             0
## 27    27        CD8 Thymus CA Exp03       numeric   114630             0
## 28    28      CD8 Thymus CAKR Exp02       numeric   114630             0
## 29    29      CD8 Thymus CAKR Exp03       numeric   114630             0
## 30    30        CD8 Thymus WT Exp01       numeric   114630             0
## 31    31        CD8 Thymus WT Exp02       numeric   114630             0
## 32    32        CD8 Thymus WT Exp03       numeric   114630             0
##    Per_of_Missing No_of_distinct_values
## 1           0.000                114630
## 2           0.000                  2972
## 3           0.958                   110
## 4           0.000                 31170
## 5           0.000                    27
## 6           0.000                    21
## 7           0.000                    36
## 8           0.000                    25
## 9           0.000                    34
## 10          0.000                    28
## 11          0.000                    34
## 12          0.000                    12
## 13          0.000                    29
## 14          0.000                    11
## 15          0.000                    10
## 16          0.000                    18
## 17          0.000                    28
## 18          0.000                    25
## 19          0.000                    21
## 20          0.000                    23
## 21          0.000                    17
## 22          0.000                    25
## 23          0.000                    26
## 24          0.000                    31
## 25          0.000                    30
## 26          0.000                     9
## 27          0.000                    23
## 28          0.000                     9
## 29          0.000                    11
## 30          0.000                    22
## 31          0.000                     9
## 32          0.000                    30

Prop table TRB

excel_count_table_trb3 <- excel_count_table_trb2 %>% 
  mutate(nkt_trav11_traj18 = "no")

count_table_trb4 <- as.matrix(excel_count_table_trb3[,5:32])
rownames(count_table_trb4) <- excel_count_table_trb3$aaSeqCDR3


trb4_norm <- scale(count_table_trb4, center=FALSE, scale=colSums(count_table_trb4))

prop.table.trb <- cbind(trb4_norm, excel_count_table_trb %>% select(-starts_with("CD")) ) 
colnames(prop.table.trb)
##  [1] "CD4 Lymph nodes CA Exp01"   "CD4 Lymph nodes CA Exp02"  
##  [3] "CD4 Lymph nodes CA Exp03"   "CD4 Lymph nodes CAKR Exp02"
##  [5] "CD4 Lymph nodes CAKR Exp03" "CD4 Lymph nodes WT Exp02"  
##  [7] "CD4 Lymph nodes WT Exp03"   "CD4 Thymus CA Exp01"       
##  [9] "CD4 Thymus CA Exp03"        "CD4 Thymus CAKR Exp01"     
## [11] "CD4 Thymus CAKR Exp02"      "CD4 Thymus CAKR Exp03"     
## [13] "CD4 Thymus WT Exp02"        "CD4 Thymus WT Exp03"       
## [15] "CD8 Lymph nodes CA Exp02"   "CD8 Lymph nodes CA Exp03"  
## [17] "CD8 Lymph nodes CAKR Exp02" "CD8 Lymph nodes CAKR Exp03"
## [19] "CD8 Lymph nodes WT Exp01"   "CD8 Lymph nodes WT Exp02"  
## [21] "CD8 Lymph nodes WT Exp03"   "CD8 Thymus CA Exp01"       
## [23] "CD8 Thymus CA Exp03"        "CD8 Thymus CAKR Exp02"     
## [25] "CD8 Thymus CAKR Exp03"      "CD8 Thymus WT Exp01"       
## [27] "CD8 Thymus WT Exp02"        "CD8 Thymus WT Exp03"       
## [29] "aaSeqCDR3"                  "allVHitsWithScore"         
## [31] "allDHitsWithScore"          "allJHitsWithScore"
prop.table.trb2 <- prop.table.trb %>% dplyr::select(29, 31, 30, 32, 1:28)


prop.table.trb2 %>% head
##                     aaSeqCDR3 allDHitsWithScore
## CASSDSAETLYF     CASSDSAETLYF              <NA>
## CASSDAEQFF         CASSDAEQFF              <NA>
## CASSDAGYEQYF     CASSDAGYEQYF              <NA>
## CASSDWGGYAEQFF CASSDWGGYAEQFF      TRBD2*00(45)
## CASSDSGGQDTQYF CASSDSGGQDTQYF      TRBD2*00(35)
## CASRDRNTEVFF     CASRDRNTEVFF      TRBD1*00(30)
##                                    allVHitsWithScore allJHitsWithScore
## CASSDSAETLYF   TRBV13-3*00(755.1),TRBV13-1*00(664.5)   TRBJ2-3*00(245)
## CASSDAEQFF                        TRBV13-1*00(819.6) TRBJ2-1*00(219.2)
## CASSDAGYEQYF                     TRBV13-1*00(1283.8)   TRBJ2-7*00(215)
## CASSDWGGYAEQFF                      TRBV13-1*00(676)   TRBJ2-1*00(235)
## CASSDSGGQDTQYF                    TRBV13-3*00(886.3)   TRBJ2-5*00(235)
## CASRDRNTEVFF                        TRBV13-3*00(979) TRBJ1-1*00(232.7)
##                CD4 Lymph nodes CA Exp01 CD4 Lymph nodes CA Exp02
## CASSDSAETLYF               1.729847e-04             5.154108e-05
## CASSDAEQFF                 0.000000e+00             0.000000e+00
## CASSDAGYEQYF               0.000000e+00             5.154108e-05
## CASSDWGGYAEQFF             7.413631e-05             5.154108e-05
## CASSDSGGQDTQYF             2.471210e-05             0.000000e+00
## CASRDRNTEVFF               1.482726e-04             5.154108e-05
##                CD4 Lymph nodes CA Exp03 CD4 Lymph nodes CAKR Exp02
## CASSDSAETLYF               1.141010e-04               3.152287e-05
## CASSDAEQFF                 9.508415e-05               0.000000e+00
## CASSDAGYEQYF               3.803366e-05               0.000000e+00
## CASSDWGGYAEQFF             1.901683e-05               1.260915e-04
## CASSDSGGQDTQYF             3.803366e-05               3.152287e-05
## CASRDRNTEVFF               5.705049e-05               9.456861e-05
##                CD4 Lymph nodes CAKR Exp03 CD4 Lymph nodes WT Exp02
## CASSDSAETLYF                 2.747190e-04             2.637618e-05
## CASSDAEQFF                   2.289325e-05             5.275235e-05
## CASSDAGYEQYF                 6.867975e-05             2.637618e-04
## CASSDWGGYAEQFF               0.000000e+00             0.000000e+00
## CASSDSGGQDTQYF               0.000000e+00             0.000000e+00
## CASRDRNTEVFF                 0.000000e+00             0.000000e+00
##                CD4 Lymph nodes WT Exp03 CD4 Thymus CA Exp01 CD4 Thymus CA Exp03
## CASSDSAETLYF               2.293368e-04        2.366397e-04        6.682259e-05
## CASSDAEQFF                 2.293368e-05        7.887991e-05        4.454839e-05
## CASSDAGYEQYF               2.293368e-05        3.549596e-04        1.113710e-04
## CASSDWGGYAEQFF             0.000000e+00        1.577598e-04        2.227420e-05
## CASSDSGGQDTQYF             0.000000e+00        0.000000e+00        0.000000e+00
## CASRDRNTEVFF               6.880103e-05        0.000000e+00        4.454839e-05
##                CD4 Thymus CAKR Exp01 CD4 Thymus CAKR Exp02
## CASSDSAETLYF            8.731718e-05                     0
## CASSDAEQFF              0.000000e+00                     0
## CASSDAGYEQYF            4.365859e-05                     0
## CASSDWGGYAEQFF          0.000000e+00                     0
## CASSDSGGQDTQYF          0.000000e+00                     0
## CASRDRNTEVFF            0.000000e+00                     0
##                CD4 Thymus CAKR Exp03 CD4 Thymus WT Exp02 CD4 Thymus WT Exp03
## CASSDSAETLYF            0.0000000000        4.972527e-05        2.538554e-04
## CASSDAEQFF              0.0000000000        2.486263e-05        3.173193e-05
## CASSDAGYEQYF            0.0000300075        2.486263e-05        0.000000e+00
## CASSDWGGYAEQFF          0.0000000000        2.486263e-05        3.173193e-05
## CASSDSGGQDTQYF          0.0000000000        2.486263e-05        0.000000e+00
## CASRDRNTEVFF            0.0002100525        0.000000e+00        3.173193e-05
##                CD8 Lymph nodes CA Exp02 CD8 Lymph nodes CA Exp03
## CASSDSAETLYF               3.846495e-04             3.699984e-04
## CASSDAEQFF                 2.662958e-04             1.849992e-04
## CASSDAGYEQYF               3.254727e-04             2.642846e-04
## CASSDWGGYAEQFF             1.183537e-04             2.642846e-04
## CASSDSGGQDTQYF             5.917685e-05             5.285692e-05
## CASRDRNTEVFF               2.367074e-04             5.285692e-05
##                CD8 Lymph nodes CAKR Exp02 CD8 Lymph nodes CAKR Exp03
## CASSDSAETLYF                 3.195909e-04               2.544113e-04
## CASSDAEQFF                   1.452686e-04               7.268895e-05
## CASSDAGYEQYF                 3.776984e-04               2.544113e-04
## CASSDWGGYAEQFF               9.878265e-04               4.179614e-04
## CASSDSGGQDTQYF               2.905372e-05               7.268895e-05
## CASRDRNTEVFF                 1.452686e-04               1.272057e-04
##                CD8 Lymph nodes WT Exp01 CD8 Lymph nodes WT Exp02
## CASSDSAETLYF               0.0005254621             3.488707e-04
## CASSDAEQFF                 0.0002741541             1.365146e-04
## CASSDAGYEQYF               0.0005483082             6.219000e-04
## CASSDWGGYAEQFF             0.0003198465             2.730293e-04
## CASSDSGGQDTQYF             0.0003198465             4.550488e-05
## CASRDRNTEVFF               0.0002056156             4.550488e-05
##                CD8 Lymph nodes WT Exp03 CD8 Thymus CA Exp01 CD8 Thymus CA Exp03
## CASSDSAETLYF               0.0002286917        0.0003316475        1.856493e-04
## CASSDAEQFF                 0.0001829533        0.0002487356        9.282465e-05
## CASSDAGYEQYF               0.0005031216        0.0003316475        1.856493e-04
## CASSDWGGYAEQFF             0.0002058225        0.0001658237        3.094155e-05
## CASSDSGGQDTQYF             0.0000000000        0.0000000000        3.094155e-05
## CASRDRNTEVFF               0.0001372150        0.0000000000        1.237662e-04
##                CD8 Thymus CAKR Exp02 CD8 Thymus CAKR Exp03 CD8 Thymus WT Exp01
## CASSDSAETLYF            0.0000000000          0.0000000000        1.804457e-04
## CASSDAEQFF              0.0002597628          0.0002214839        3.308171e-04
## CASSDAGYEQYF            0.0000000000          0.0001661130        4.210400e-04
## CASSDWGGYAEQFF          0.0003463503          0.0002214839        2.706686e-04
## CASSDSGGQDTQYF          0.0000000000          0.0001107420        6.014857e-05
## CASRDRNTEVFF            0.0000000000          0.0000000000        1.202971e-04
##                CD8 Thymus WT Exp02 CD8 Thymus WT Exp03
## CASSDSAETLYF          4.479484e-04        2.347268e-04
## CASSDAEQFF            2.687690e-04        8.535518e-05
## CASSDAGYEQYF          3.583587e-04        2.774043e-04
## CASSDWGGYAEQFF        2.687690e-04        6.401639e-05
## CASSDSGGQDTQYF        0.000000e+00        2.133880e-05
## CASRDRNTEVFF          8.958968e-05        1.066940e-04
ExpData(data=prop.table.trb2, type=2)
##    Index              Variable_Name Variable_Type Sample_n Missing_Count
## 1      1                  aaSeqCDR3     character   426800             0
## 2      2          allDHitsWithScore     character   331672         95128
## 3      3          allVHitsWithScore     character   426800             0
## 4      4          allJHitsWithScore     character   426800             0
## 5      5   CD4 Lymph nodes CA Exp01       numeric   426800             0
## 6      6   CD4 Lymph nodes CA Exp02       numeric   426800             0
## 7      7   CD4 Lymph nodes CA Exp03       numeric   426800             0
## 8      8 CD4 Lymph nodes CAKR Exp02       numeric   426800             0
## 9      9 CD4 Lymph nodes CAKR Exp03       numeric   426800             0
## 10    10   CD4 Lymph nodes WT Exp02       numeric   426800             0
## 11    11   CD4 Lymph nodes WT Exp03       numeric   426800             0
## 12    12        CD4 Thymus CA Exp01       numeric   426800             0
## 13    13        CD4 Thymus CA Exp03       numeric   426800             0
## 14    14      CD4 Thymus CAKR Exp01       numeric   426800             0
## 15    15      CD4 Thymus CAKR Exp02       numeric   426800             0
## 16    16      CD4 Thymus CAKR Exp03       numeric   426800             0
## 17    17        CD4 Thymus WT Exp02       numeric   426800             0
## 18    18        CD4 Thymus WT Exp03       numeric   426800             0
## 19    19   CD8 Lymph nodes CA Exp02       numeric   426800             0
## 20    20   CD8 Lymph nodes CA Exp03       numeric   426800             0
## 21    21 CD8 Lymph nodes CAKR Exp02       numeric   426800             0
## 22    22 CD8 Lymph nodes CAKR Exp03       numeric   426800             0
## 23    23   CD8 Lymph nodes WT Exp01       numeric   426800             0
## 24    24   CD8 Lymph nodes WT Exp02       numeric   426800             0
## 25    25   CD8 Lymph nodes WT Exp03       numeric   426800             0
## 26    26        CD8 Thymus CA Exp01       numeric   426800             0
## 27    27        CD8 Thymus CA Exp03       numeric   426800             0
## 28    28      CD8 Thymus CAKR Exp02       numeric   426800             0
## 29    29      CD8 Thymus CAKR Exp03       numeric   426800             0
## 30    30        CD8 Thymus WT Exp01       numeric   426800             0
## 31    31        CD8 Thymus WT Exp02       numeric   426800             0
## 32    32        CD8 Thymus WT Exp03       numeric   426800             0
##    Per_of_Missing No_of_distinct_values
## 1           0.000                426800
## 2           0.223                   214
## 3           0.000                 23763
## 4           0.000                  2168
## 5           0.000                    29
## 6           0.000                    21
## 7           0.000                    33
## 8           0.000                    20
## 9           0.000                    38
## 10          0.000                    30
## 11          0.000                    25
## 12          0.000                    24
## 13          0.000                    42
## 14          0.000                    19
## 15          0.000                    14
## 16          0.000                    39
## 17          0.000                    17
## 18          0.000                    15
## 19          0.000                    24
## 20          0.000                    25
## 21          0.000                    24
## 22          0.000                    32
## 23          0.000                    22
## 24          0.000                    32
## 25          0.000                    23
## 26          0.000                     9
## 27          0.000                    20
## 28          0.000                    14
## 29          0.000                    14
## 30          0.000                    16
## 31          0.000                     9
## 32          0.000                    20

Gene segment usage analysis

immdata_tra <- readRDS("immdata_tra.rds")
immdata_trb <- readRDS("immdata_trb.rds")

TRA

### Thymus

cd4_thymus <- geneUsage(repFilter(immdata_tra, 
                                 .method = "by.meta", 
                                 .query = list(Organ = include('Thymus'), Cell_type = include('CD4')))$data, .norm = T, .ambig = "maj", .quant = "count", .gene = "musmus.trav")

vis(cd4_thymus, .by = c("Cell_type", "Organ","Mouse_strain"), .meta = immdata_tra$meta) + ggtitle("TRA Thymus CD4") + scale_fill_manual(values = c("dodgerblue","indianred2","gray70"))

#ggsave("./final_fig/gene_usage_new/cd4_thymus_tra_just1segment.png", width = 60, height = 12, units = "cm") 
#ggsave("./final_fig/gene_usage_new/cd4_thymus_tra_just1segment.svg", width = 60, height = 12, units = "cm") 


### LN
cd4_ln <- geneUsage(repFilter(immdata_tra, 
                                 .method = "by.meta", 
                                 .query = list(Organ = exclude('Thymus'), Cell_type = include('CD4')))$data, .norm = T, .ambig = "maj", .quant = "count", .gene = "musmus.trav")

vis(cd4_ln, .by = c("Cell_type", "Organ","Mouse_strain"), .meta = immdata_tra$meta) + ggtitle("TRA LN CD4") + scale_fill_manual(values = c("dodgerblue","indianred2","gray70"))

#ggsave("./final_fig/gene_usage_new/cd4_ln_tra_just1segment.png", width = 60, height = 12, units = "cm")
#ggsave("./final_fig/gene_usage_new/cd4_ln_tra_just1segment.svg", width = 60, height = 12, units = "cm")

cd8_thymus <- geneUsage(repFilter(immdata_tra, 
                                 .method = "by.meta", 
                                 .query = list(Organ = include('Thymus'), Cell_type = include('CD8')))$data, .norm = T, .ambig = "maj", .quant = "count", .gene = "musmus.trav")

vis(cd8_thymus, .by = c("Cell_type", "Organ","Mouse_strain"), .meta = immdata_tra$meta) + ggtitle("TRA Thymus CD8") + scale_fill_manual(values = c("dodgerblue","indianred2","gray70"))

#ggsave("./final_fig/gene_usage_new/cd8_thymus_tra_just1segment.png", width = 60, height = 12, units = "cm")
#ggsave("./final_fig/gene_usage_new/cd8_thymus_tra_just1segment.svg", width = 60, height = 12, units = "cm")


cd8_ln <- geneUsage(repFilter(immdata_tra, 
                                 .method = "by.meta", 
                                 .query = list(Organ = exclude('Thymus'), Cell_type = include('CD8')))$data, .norm = T, .ambig = "maj", .quant = "count", .gene = "musmus.trav")

vis(cd8_ln, .by = c("Cell_type", "Organ","Mouse_strain"), .meta = immdata_tra$meta) + ggtitle("TRA LN CD8") + scale_fill_manual(values = c("dodgerblue","indianred2","gray70"))

#ggsave("./final_fig/gene_usage_new/cd8_ln_tra_just1segment.png", width = 60, height = 12, units = "cm")
#ggsave("./final_fig/gene_usage_new/cd8_ln_tra_just1segment.svg", width = 60, height = 12, units = "cm")

TRB

### Thymus

cd4_thymus <- geneUsage(repFilter(immdata_trb, 
                                 .method = "by.meta", 
                                 .query = list(Organ = include('Thymus'), Cell_type = include('CD4')))$data, .norm = T, .ambig = "maj", .quant = "count", .gene = "musmus.trbv")

vis(cd4_thymus, .by = c("Cell_type", "Organ","Mouse_strain"), .meta = immdata_trb$meta, .test = F) + ggtitle("TRB Thymus CD4") + scale_fill_manual(values = c("dodgerblue","indianred2","gray70")) 

#ggsave("./final_fig/gene_usage_new/cd4_thymus_trb_just1segment.png", width = 20, height = 10, units = "cm") 
#ggsave("./final_fig/gene_usage_new/cd4_thymus_trb_just1segment.svg", width = 20, height = 10, units = "cm")

### LN
cd4_ln <- geneUsage(repFilter(immdata_trb, 
                                 .method = "by.meta", 
                                 .query = list(Organ = exclude('Thymus'), Cell_type = include('CD4')))$data, .norm = T, .ambig = "maj", .quant = "count", .gene = "musmus.trbv")

vis(cd4_ln, .by = c("Cell_type", "Organ","Mouse_strain"), .meta = immdata_trb$meta, .test = F) + ggtitle("TRB LN CD4") + scale_fill_manual(values = c("dodgerblue","indianred2","gray70"))

#ggsave("./final_fig/gene_usage_new/cd4_ln_trb_just1segment.png", width = 20, height = 10, units = "cm")
#ggsave("./final_fig/gene_usage_new/cd4_ln_trb_just1segment.svg", width = 20, height = 10, units = "cm")


cd8_thymus <- geneUsage(repFilter(immdata_trb, 
                                 .method = "by.meta", 
                                 .query = list(Organ = include('Thymus'), Cell_type = include('CD8')))$data, .norm = T, .ambig = "maj", .quant = "count", .gene = "musmus.trbv")

vis(cd8_thymus, .by = c("Cell_type", "Organ","Mouse_strain"), .meta = immdata_trb$meta, .test = F) + ggtitle("TRB Thymus CD8") + scale_fill_manual(values = c("dodgerblue","indianred2","gray70"))

#ggsave("./final_fig/gene_usage_new/cd8_thymus_trb_just1segment.png", width = 20, height = 10, units = "cm")
#ggsave("./final_fig/gene_usage_new/cd8_thymus_trb_just1segment.svg", width = 20, height = 10, units = "cm")


cd8_ln <- geneUsage(repFilter(immdata_trb, 
                                 .method = "by.meta", 
                                 .query = list(Organ = exclude('Thymus'), Cell_type = include('CD8')))$data, .norm = T, .ambig = "maj", .quant = "count", .gene = "musmus.trbv")

vis(cd8_ln, .by = c("Cell_type", "Organ","Mouse_strain"), .meta = immdata_trb$meta, .test = F) + ggtitle("TRB LN CD8") + scale_fill_manual(values = c("dodgerblue","indianred2","gray70"))

#ggsave("./final_fig/gene_usage_new/cd8_ln_trb_just1segment.png", width = 20, height = 10, units = "cm")
#ggsave("./final_fig/gene_usage_new/cd8_ln_trb_just1segment.svg", width = 20, height = 10, units = "cm")

Analysis of NKT cells

Next, we will identify gene segments that are typical for invariant NKT cells. These segments are TRAV11 and TRAJ18 for TRA and TRBV1, TRBV13-2 and TRBV29 for TRB. Please, note that some of the included TRB chains are used by conventional cells as well.

# NKT analysis
merged_repertoires <- merged_repertoires %>% 
  mutate(is_nkt = if_else(
    (grepl(allVHitsWithScore, pattern = "TRAV11")) & (grepl(allJHitsWithScore, pattern = "TRAJ18")) |
    (grepl(allVHitsWithScore, pattern = "TRBV13-2")) |
      (grepl(allVHitsWithScore, pattern = "TRBV1\\*")) |
      (grepl(allVHitsWithScore, pattern = "TRBV29"))  ,"yes","no")) %>% 
  mutate(nkt_trav11_traj18 = if_else(
    (grepl(allVHitsWithScore, pattern = "TRAV11") & (grepl(allJHitsWithScore, pattern = "TRAJ18"))),"yes","no")) %>% mutate(nkt_trbv13_2 = if_else(
    (grepl(allVHitsWithScore, pattern = "TRBV13-2")),"yes","no")) %>% mutate(nkt_trbv29 = if_else(
    (grepl(allVHitsWithScore, pattern = "TRBV29")),"yes","no")) %>% mutate(nkt_trbv1 = if_else(
    (grepl(allVHitsWithScore, pattern = "TRBV1\\*")),"yes","no"))

md <- md %>% select(Exp, Organ, Cell_type, Mouse_strain, num_id) %>% mutate_at(vars("num_id"), as.numeric)

# All TRB NKT
nkt_table_trb <- merged_repertoires %>% 
   filter(chain == "TRB") %>% 
 mutate(new_name = paste(Exp, Organ, Cell_type, Mouse_strain)) %>% 
  dplyr::select(new_name, cloneCount, is_nkt, num_id) %>% 
  uncount(cloneCount) %>% 
  group_by(num_id, is_nkt) %>% 
    summarise(n = n()) %>% 
mutate(freq = n / sum(n)) %>% dplyr::filter(is_nkt == "yes") %>% 
  ungroup %>% 
  left_join(md) %>% 
  unique %>%
   mutate(sample_type = paste(Cell_type, Organ, Mouse_strain)) 

# TRAV11 TRAJ18
nkt_table_trav11_traj18 <- merged_repertoires %>% 
   filter(chain == "TRA") %>% 
 mutate(new_name = paste(Exp, Organ, Cell_type, Mouse_strain)) %>% 
  dplyr::select(new_name, cloneCount, nkt_trav11_traj18, num_id) %>% 
  uncount(cloneCount) %>% 
  group_by(num_id, nkt_trav11_traj18) %>% 
    summarise(n = n()) %>% 
mutate(freq = n / sum(n)) %>% dplyr::filter(nkt_trav11_traj18 == "yes") %>% 
  ungroup %>% 
  left_join(md) %>% 
  unique %>%
   mutate(sample_type = paste(Cell_type, Organ, Mouse_strain)) 

# TRBV1
nkt_table_trbv1 <- merged_repertoires %>% 
   filter(chain == "TRB") %>% 
 mutate(new_name = paste(Exp, Organ, Cell_type, Mouse_strain)) %>% 
  dplyr::select(new_name, cloneCount, nkt_trbv1, num_id) %>% 
  uncount(cloneCount) %>% 
  group_by(num_id, nkt_trbv1) %>% 
    summarise(n = n()) %>% 
mutate(freq = n / sum(n)) %>% dplyr::filter(nkt_trbv1 == "yes") %>% 
  ungroup %>% 
  left_join(md) %>% 
  unique %>%
   mutate(sample_type = paste(Cell_type, Organ, Mouse_strain)) 

# TRBV13-2
nkt_table_trbv13_2 <- merged_repertoires %>% 
   filter(chain == "TRB") %>% 
 mutate(new_name = paste(Exp, Organ, Cell_type, Mouse_strain)) %>% 
  dplyr::select(new_name, cloneCount, nkt_trbv13_2, num_id) %>% 
  uncount(cloneCount) %>% 
  group_by(num_id, nkt_trbv13_2) %>% 
    summarise(n = n()) %>% 
mutate(freq = n / sum(n)) %>% dplyr::filter(nkt_trbv13_2 == "yes") %>% 
  ungroup %>% 
  left_join(md) %>% 
  unique %>%
   mutate(sample_type = paste(Cell_type, Organ, Mouse_strain)) 


# TRBV29
nkt_table_trbv29 <- merged_repertoires %>% 
   filter(chain == "TRB") %>% 
 mutate(new_name = paste(Exp, Organ, Cell_type, Mouse_strain)) %>% 
  dplyr::select(new_name, cloneCount, nkt_trbv29, num_id) %>% 
  uncount(cloneCount) %>% 
  group_by(num_id, nkt_trbv29) %>% 
    summarise(n = n()) %>% 
mutate(freq = n / sum(n)) %>% dplyr::filter(nkt_trbv29 == "yes") %>% 
  ungroup %>% 
  left_join(md) %>% 
  unique %>%
   mutate(sample_type = paste(Cell_type, Organ, Mouse_strain)) 

levels_cd4 <- c("CD4 Thymus WT", "CD4 Thymus CA", "CD4 Thymus CAKR", "CD4 Lymph nodes WT", "CD4 Lymph nodes CA", "CD4 Lymph nodes CAKR")
levels_cd8 <- c("CD8 Thymus WT", "CD8 Thymus CA", "CD8 Thymus CAKR", "CD8 Lymph nodes WT", "CD8 Lymph nodes CA", "CD8 Lymph nodes CAKR")

nkt_table_trb$nkt_sample <- "all_nkt_trb"
nkt_table_trav11_traj18$nkt_sample <- "trav11_traj18"
nkt_table_trbv1$nkt_sample <- "trbv1"
nkt_table_trbv13_2$nkt_sample <- "trbv13_2"
nkt_table_trbv29$nkt_sample <- "trbv29"

nkt_table <- cbind(nkt_table_trb$num_id, nkt_table_trb$sample_type, nkt_table_trb$Exp, format(nkt_table_trb$freq, digits = 2), 
                   format(nkt_table_trav11_traj18$freq, digits = 2),
format(nkt_table_trbv1$freq, digits = 2), 
format(nkt_table_trbv13_2$freq, digits = 2),
                   format(nkt_table_trbv29$freq, digits = 2)) %>% as.data.frame

colnames(nkt_table) <- c("num_id","Sample","Exp","All TRB NKT", "TRAV11-TRAJ18", "TRBV1","TRBV13-2","TRBV29")
nkt_table <- nkt_table %>% mutate(Sample = paste(Sample, Exp)) %>% select(-num_id, -Exp) %>% arrange(Sample)

See the percentage of NKT gene segments in each sample:

kable(nkt_table, format = "html") %>%
  kable_styling(full_width = F, font_size = 11, 
                bootstrap_options = c("striped", "hover", "condensed", "responsive"))
Sample All TRB NKT TRAV11-TRAJ18 TRBV1 TRBV13-2 TRBV29
CD4 Lymph nodes CA 1 0.18 0.02574 0.032 0.120 0.027
CD4 Lymph nodes CA 2 0.19 0.02326 0.031 0.124 0.031
CD4 Lymph nodes CA 3 0.16 0.02046 0.024 0.109 0.028
CD4 Lymph nodes CAKR 2 0.19 0.01704 0.031 0.127 0.029
CD4 Lymph nodes CAKR 3 0.18 0.02357 0.034 0.120 0.030
CD4 Lymph nodes WT 2 0.17 0.00474 0.028 0.114 0.029
CD4 Lymph nodes WT 3 0.17 0.00307 0.031 0.111 0.029
CD4 Thymus CA 1 0.36 0.25798 0.032 0.228 0.101
CD4 Thymus CA 3 0.38 0.26069 0.033 0.219 0.124
CD4 Thymus CAKR 1 0.30 0.22058 0.033 0.205 0.065
CD4 Thymus CAKR 2 0.32 0.38268 0.069 0.185 0.068
CD4 Thymus CAKR 3 0.38 0.23262 0.042 0.249 0.084
CD4 Thymus WT 2 0.23 0.08824 0.034 0.136 0.058
CD4 Thymus WT 3 0.19 0.06685 0.038 0.107 0.045
CD8 Lymph nodes CA 2 0.21 0.00076 0.028 0.084 0.099
CD8 Lymph nodes CA 3 0.20 0.00137 0.026 0.085 0.091
CD8 Lymph nodes CAKR 2 0.24 0.00084 0.030 0.122 0.087
CD8 Lymph nodes CAKR 3 0.20 0.00080 0.027 0.089 0.082
CD8 Lymph nodes WT 1 0.23 0.00117 0.028 0.108 0.096
CD8 Lymph nodes WT 2 0.21 0.00051 0.024 0.091 0.098
CD8 Lymph nodes WT 3 0.21 0.00091 0.024 0.089 0.095
CD8 Thymus CA 1 0.22 0.00219 0.029 0.116 0.078
CD8 Thymus CA 3 0.19 0.00102 0.024 0.084 0.078
CD8 Thymus CAKR 2 0.23 0.00521 0.028 0.124 0.076
CD8 Thymus CAKR 3 0.19 0.00188 0.032 0.105 0.056
CD8 Thymus WT 1 0.23 0.00126 0.022 0.111 0.101
CD8 Thymus WT 2 0.26 0.00115 0.027 0.129 0.102
CD8 Thymus WT 3 0.20 0.00126 0.023 0.086 0.090

Now we will plot the data for CD4 cells and for CD8 cells. Plots for TRA and TRB sequences are separated.

All NKT

This plot visualizes the percentage of any of the TRB NKT chains (TRBV1, TRBV13-2 and TRBV29). We can see that this distinction is not specific as these gene segments are also used by conventional CD4 and CD8 T cells.

## CD4
p01 <- nkt_table_trb %>% 
  filter(Cell_type == "CD4") %>% 
  ggplot(aes(y = freq*100, x = factor(sample_type, levels = levels_cd4))) +
  stat_summary(fun = "mean",
               geom = "crossbar", 
               width = 0.5,
               colour = "black") +
  geom_point(aes(color = Mouse_strain)) + 
  ylab("Percentage of all chains") + 
  ggtitle("NKT segments in CD4 TRB") + 
  theme_classic() + 
  ggtheme() + 
  scale_color_manual(values = c("dodgerblue","indianred2","gray40")) + 
  ylim(c(0,NA)) + xlab("")
  
#ggsave("./final_fig/nkt/pct_all_nkt_trb_cd4.png", width = 8, height = 8, units = "cm")
#ggsave("./final_fig/nkt/pct_all_nkt_trb_cd4.eps", width = 8, height = 8, units = "cm")

## CD8
p02 <- nkt_table_trb %>% 
  filter(Cell_type == "CD8") %>% 
  ggplot(aes(y = freq*100, x = factor(sample_type, levels = levels_cd8))) +
  stat_summary(fun = "mean",
               geom = "crossbar", 
               width = 0.5,
               colour = "black") +
  geom_point(aes(color = Mouse_strain)) + 
  ylab("Percentage of all chains") + 
  ggtitle("NKT segments in CD8 TRB") + 
  theme_classic() + 
  ggtheme() + 
  scale_color_manual(values = c("dodgerblue","indianred2","gray40")) + 
  ylim(c(0,NA)) + xlab("")
  
#ggsave("./final_fig/nkt/pct_all_nkt_trb_cd8.png", width = 8, height = 8, units = "cm")
#ggsave("./final_fig/nkt/pct_all_nkt_trb_cd8.eps", width = 8, height = 8, units = "cm")

p01 + p02

TRAV11 TRAJ18

## CD4
p03 <- nkt_table_trav11_traj18 %>% 
  filter(Cell_type == "CD4") %>% 
  ggplot(aes(y = freq*100, x = factor(sample_type, levels = levels_cd4))) +
  stat_summary(fun = "mean",
               geom = "crossbar", 
               width = 0.5,
               colour = "black") +
  geom_point(aes(color = Mouse_strain)) + 
  ylab("Percentage of all chains") + 
  ggtitle("TRAV11-TRAJ18 segments in CD4 cells") + 
  theme_classic() + 
  ggtheme() + 
  scale_color_manual(values = c("dodgerblue","indianred2","gray40")) + 
  ylim(c(0,NA)) + xlab("")
  
#ggsave("./final_fig/nkt/pct_nkt_trav11_traj18_tra_cd4.png", width = 8, height = 8, units = "cm")
#ggsave("./final_fig/nkt/pct_nkt_trav11_traj18_tra_cd4.eps", width = 8, height = 8, units = "cm")

## CD8
p04 <- nkt_table_trav11_traj18 %>% 
  filter(Cell_type == "CD8") %>% 
  ggplot(aes(y = freq*100, x = factor(sample_type, levels = levels_cd8))) +
  stat_summary(fun = "mean",
               geom = "crossbar", 
               width = 0.5,
               colour = "black") +
  geom_point(aes(color = Mouse_strain)) + 
  ylab("Percentage of all chains") + 
  ggtitle("TRAV11-TRAJ18 segments in CD8 cells") + 
  theme_classic() + 
  ggtheme() + 
  scale_color_manual(values = c("dodgerblue","indianred2","gray40")) + 
  ylim(c(0,1)) + xlab("")
  
#ggsave("./final_fig/nkt/pct_nkt_trav11_traj18_tra_cd8.png", width = 8, height = 8, units = "cm")
#ggsave("./final_fig/nkt/pct_nkt_trav11_traj18_tra_cd8.eps", width = 8, height = 8, units = "cm")

p03 + p04

TRBV1

## CD4
p05 <- nkt_table_trbv1 %>% 
  filter(Cell_type == "CD4") %>% 
  ggplot(aes(y = freq*100, x = factor(sample_type, levels = levels_cd4))) +
  stat_summary(fun = "mean",
               geom = "crossbar", 
               width = 0.5,
               colour = "black") +
  geom_point(aes(color = Mouse_strain)) + 
  ylab("Percentage of all chains") + 
  ggtitle("CD4 TRBV1") + 
  theme_classic() + 
  ggtheme() + 
  scale_color_manual(values = c("dodgerblue","indianred2","gray40")) + 
  ylim(c(0,NA)) + xlab("")
  
#ggsave("./final_fig/nkt/pct_nkt_trbv1_cd4.png", width = 8, height = 8, units = "cm")
#ggsave("./final_fig/nkt/pct_nkt_trbv1_cd4.eps", width = 8, height = 8, units = "cm")

## CD8
p06 <- nkt_table_trbv1 %>% 
  filter(Cell_type == "CD8") %>% 
  ggplot(aes(y = freq*100, x = factor(sample_type, levels = levels_cd8))) +
  stat_summary(fun = "mean",
               geom = "crossbar", 
               width = 0.5,
               colour = "black") +
  geom_point(aes(color = Mouse_strain)) + 
  ylab("Percentage of all chains") + 
  ggtitle("CD8 TRBV1") + 
  theme_classic() + 
  ggtheme() + 
  
  scale_color_manual(values = c("dodgerblue","indianred2","gray40")) + 
  ylim(c(0,NA)) + xlab("")
  
#ggsave("./final_fig/nkt/pct_nkt_trbv1_cd8.png", width = 8, height = 8, units = "cm")
#ggsave("./final_fig/nkt/pct_nkt_trbv1_cd8.eps", width = 8, height = 8, units = "cm")

p05 + p06

TRBV13-2

## CD4
p07 <- nkt_table_trbv13_2 %>% 
  filter(Cell_type == "CD4") %>% 
  ggplot(aes(y = freq*100, x = factor(sample_type, levels = levels_cd4))) +
  stat_summary(fun = "mean",
               geom = "crossbar", 
               width = 0.5,
               colour = "black") +
  geom_point(aes(color = Mouse_strain)) + 
  ylab("Percentage of all chains") + 
  ggtitle("TRBV13-2 segments in CD4 cells") + 
  theme_classic() + 
  ggtheme() + 
  scale_color_manual(values = c("dodgerblue","indianred2","gray40")) + 
  ylim(c(0,NA)) + xlab("")
  
#ggsave("./final_fig/nkt/pct_nkt_trbv13_2_cd4.png", width = 8, height = 8, units = "cm")
#ggsave("./final_fig/nkt/pct_nkt_trbv13_2_cd4.eps", width = 8, height = 8, units = "cm")

## CD8
p08 <- nkt_table_trbv13_2 %>% 
  filter(Cell_type == "CD8") %>% 
  ggplot(aes(y = freq*100, x = factor(sample_type, levels = levels_cd8))) +
  stat_summary(fun = "mean",
               geom = "crossbar", 
               width = 0.5,
               colour = "black") +
  geom_point(aes(color = Mouse_strain)) + 
  ylab("Percentage of all chains") + 
  ggtitle("TRBV13-2 segments in CD8 cells") + 
  theme_classic() + 
  ggtheme() + 
  
  scale_color_manual(values = c("dodgerblue","indianred2","gray40")) + 
  ylim(c(0,NA)) + xlab("")
  
#ggsave("./final_fig/nkt/pct_nkt_trbv13_2_cd8.png", width = 8, height = 8, units = "cm")
#ggsave("./final_fig/nkt/pct_nkt_trbv13_2_cd8.eps", width = 8, height = 8, units = "cm")

p07 + p08

TRBV29

## CD4
p09 <- nkt_table_trbv29 %>% 
  filter(Cell_type == "CD4") %>% 
  ggplot(aes(y = freq*100, x = factor(sample_type, levels = levels_cd4))) +
  stat_summary(fun = "mean",
               geom = "crossbar", 
               width = 0.5,
               colour = "black") +
  geom_point(aes(color = Mouse_strain)) + 
  ylab("Percentage of all chains") + 
  ggtitle("CD4 TRBV29") + 
  theme_classic() + 
  ggtheme() + 
  scale_color_manual(values = c("dodgerblue","indianred2","gray40")) + 
  ylim(c(0,NA)) + xlab("")
  
#ggsave("./final_fig/nkt/pct_nkt_trbv29_cd4.png", width = 8, height = 8, units = "cm")
#ggsave("./final_fig/nkt/pct_nkt_trbv29_cd4.eps", width = 8, height = 8, units = "cm")

## CD8
p10 <- nkt_table_trbv29 %>% 
  filter(Cell_type == "CD8") %>% 
  ggplot(aes(y = freq*100, x = factor(sample_type, levels = levels_cd8))) +
  stat_summary(fun = "mean",
               geom = "crossbar", 
               width = 0.5,
               colour = "black") +
  geom_point(aes(color = Mouse_strain)) + 
  ylab("Percentage of all chains") + 
  ggtitle("CD8 TRBV29") + 
  theme_classic() + 
  ggtheme() + 
  scale_color_manual(values = c("dodgerblue","indianred2","gray40")) + 
  ylim(c(0,NA)) + xlab("")
  
#ggsave("./final_fig/nkt/pct_nkt_trbv29_cd8.png", width = 8, height = 8, units = "cm")
#ggsave("./final_fig/nkt/pct_nkt_trbv29_cd8.eps", width = 8, height = 8, units = "cm")

p09 + p10

PCA

In this part we explore the similarity of TCR repertoires of different samples using principal component analysis (PCA).

We will calculate the distances from a filtered matrix of proportions, in which we filtered out the CDR3 sequences that are present in less than five CD4 or in less than five CD8 samples.

Preparing filtered normalized count matrices

First, we will prepare the filtered normalized count matrix.

Below, we find and save the CDR3 amino acid sequences, which are present in 5 or more samples out of 14 CD4 samples, or in 5 or more samples out of 14 CD8 samples.

# TRA
keep_tra_cd4 <- excel_count_table_tra %>% 
  mutate(nkt_trav11_traj18 = if_else(
    (grepl(allVHitsWithScore, pattern = "TRAV11") | 
    (grepl(allJHitsWithScore, pattern = "TRAJ18"))),"yes","no")) %>% 
  filter(nkt_trav11_traj18 == "no") %>% 
  select(aaSeqCDR3, starts_with("CD4")) %>% 
  mutate_at(vars(starts_with("CD4")), .funs = binary) 
 
keep_tra_cd4$sum <- rowSums((keep_tra_cd4 %>% select(-aaSeqCDR3)))

keep_tra_cd4$keep <- ifelse(keep_tra_cd4$sum>4,1,0)
keep_tra_cd4$orig <- ifelse(keep_tra_cd4$sum>0,1,0)

keep_tra_cd4_sequences <- pull(keep_tra_cd4 %>% filter(keep == 1), aaSeqCDR3)

# TRB
keep_trb_cd4 <- excel_count_table_trb %>% select(aaSeqCDR3, starts_with("CD4")) %>%  mutate_at(vars(starts_with("CD4")), .funs = binary) 
 
keep_trb_cd4$sum <- rowSums((keep_trb_cd4 %>% select(-aaSeqCDR3)))

keep_trb_cd4$keep <- ifelse(keep_trb_cd4$sum>4,1,0)
keep_trb_cd4$orig <- ifelse(keep_trb_cd4$sum>0,1,0)

keep_trb_cd4_sequences <- pull(keep_trb_cd4 %>% filter(keep == 1), aaSeqCDR3)

# TRA
keep_tra_cd8 <- excel_count_table_tra %>% 
  mutate(nkt_trav11_traj18 = if_else(
    (grepl(allVHitsWithScore, pattern = "TRAV11") | 
    (grepl(allJHitsWithScore, pattern = "TRAJ18"))),"yes","no")) %>% 
  filter(nkt_trav11_traj18 == "no") %>% 
  select(aaSeqCDR3, starts_with("CD8")) %>% 
  mutate_at(vars(starts_with("CD8")), .funs = binary) 

keep_tra_cd8$sum <- rowSums((keep_tra_cd8 %>% select(-aaSeqCDR3)))

keep_tra_cd8$keep <- ifelse(keep_tra_cd8$sum>4,1,0)
keep_tra_cd8$orig <- ifelse(keep_tra_cd8$sum>0,1,0)

keep_tra_cd8_sequences <- pull(keep_tra_cd8 %>% filter(keep == 1), aaSeqCDR3)

# TRB
keep_trb_cd8 <- excel_count_table_trb %>% select(aaSeqCDR3, starts_with("CD8")) %>%  mutate_at(vars(starts_with("CD8")), .funs = binary) 
 
keep_trb_cd8$sum <- rowSums((keep_trb_cd8 %>% select(-aaSeqCDR3)))

keep_trb_cd8$keep <- ifelse(keep_trb_cd8$sum>4,1,0)
keep_trb_cd8$orig <- ifelse(keep_trb_cd8$sum>0,1,0)

keep_trb_cd8_sequences <- pull(keep_trb_cd8 %>% filter(keep == 1), aaSeqCDR3)


cdr3_count_table <- data.frame(Sample = c("CD4 TRA","CD4 TRB","CD8 TRA","CD8 TRB"),
                               `Count before filtering` = c(
                                 length(pull(keep_tra_cd4 %>% filter(orig == 1), aaSeqCDR3)),
                                 length(pull(keep_trb_cd4 %>% filter(orig == 1), aaSeqCDR3)),
                                 length(pull(keep_tra_cd8 %>% filter(orig == 1), aaSeqCDR3)),
                                 length(pull(keep_trb_cd8 %>% filter(orig == 1), aaSeqCDR3))),
                               `Count after filtering` = c(
                                 length(pull(keep_tra_cd4 %>% filter(keep == 1), aaSeqCDR3)),
                                 length(pull(keep_trb_cd4 %>% filter(keep == 1), aaSeqCDR3)),
                                 length(pull(keep_tra_cd8 %>% filter(keep == 1), aaSeqCDR3)),
                                 length(pull(keep_trb_cd8 %>% filter(keep == 1), aaSeqCDR3))))

Here you can check the counts of CDR3 sequences before and after filtering.

kable(cdr3_count_table) %>%
  kable_styling(full_width = F, font_size = 11, 
                bootstrap_options = c("striped", "hover", "condensed", "responsive"))
Sample Count.before.filtering Count.after.filtering
CD4 TRA 64812 3406
CD4 TRB 245611 7201
CD8 TRA 65540 3227
CD8 TRB 238965 7808

Now we will create filtered tables with the abundant sequences.

# TRB
excel_count_table_trb3_filter <- excel_count_table_trb2  %>% filter(aaSeqCDR3 %in% keep_trb_cd4_sequences | aaSeqCDR3 %in% keep_trb_cd8_sequences) 

count_table_trb4_filter <- as.matrix(excel_count_table_trb3_filter[,5:32])
rownames(count_table_trb4_filter) <- excel_count_table_trb3_filter$aaSeqCDR3

trb4_norm_filter <- scale(count_table_trb4_filter, center=FALSE, scale=colSums(count_table_trb4_filter))

prop.table.trb_filter <- cbind(trb4_norm_filter, excel_count_table_trb3_filter %>% select(-starts_with("CD")) )

# TRA
excel_count_table_tra3_filter <- excel_count_table_tra3  %>% filter(aaSeqCDR3 %in% keep_tra_cd4_sequences | aaSeqCDR3 %in% keep_tra_cd8_sequences) %>% filter(nkt_trav11_traj18 == "no")

count_table_tra4_filter <- as.matrix(excel_count_table_tra3_filter[,5:32])
rownames(count_table_tra4_filter) <- excel_count_table_tra3_filter$aaSeqCDR3

tra4_norm_filter <- scale(count_table_tra4_filter, center=FALSE, scale=colSums(count_table_tra4_filter))

prop.table.tra_filter <- cbind(tra4_norm_filter, excel_count_table_tra3_filter %>% select(-starts_with("CD")) )

colnames(prop.table.tra_filter)
##  [1] "CD4 Lymph nodes CA Exp01"   "CD4 Lymph nodes CA Exp02"  
##  [3] "CD4 Lymph nodes CA Exp03"   "CD4 Lymph nodes CAKR Exp02"
##  [5] "CD4 Lymph nodes CAKR Exp03" "CD4 Lymph nodes WT Exp02"  
##  [7] "CD4 Lymph nodes WT Exp03"   "CD4 Thymus CA Exp01"       
##  [9] "CD4 Thymus CA Exp03"        "CD4 Thymus CAKR Exp01"     
## [11] "CD4 Thymus CAKR Exp02"      "CD4 Thymus CAKR Exp03"     
## [13] "CD4 Thymus WT Exp02"        "CD4 Thymus WT Exp03"       
## [15] "CD8 Lymph nodes CA Exp02"   "CD8 Lymph nodes CA Exp03"  
## [17] "CD8 Lymph nodes CAKR Exp02" "CD8 Lymph nodes CAKR Exp03"
## [19] "CD8 Lymph nodes WT Exp01"   "CD8 Lymph nodes WT Exp02"  
## [21] "CD8 Lymph nodes WT Exp03"   "CD8 Thymus CA Exp01"       
## [23] "CD8 Thymus CA Exp03"        "CD8 Thymus CAKR Exp02"     
## [25] "CD8 Thymus CAKR Exp03"      "CD8 Thymus WT Exp01"       
## [27] "CD8 Thymus WT Exp02"        "CD8 Thymus WT Exp03"       
## [29] "aaSeqCDR3"                  "allDHitsWithScore"         
## [31] "allJHitsWithScore"          "allVHitsWithScore"         
## [33] "nkt_trav11_traj18"

Last, we will select the columns of the filtered table corresponding to CD4 and CD8 cells. We will remove the rows in which there are only zeros for the subset of CD4 or CD8 cells, respectively.

prop.table.tra2_filter.cd4 <- prop.table.tra_filter[,1:14]
prop.table.tra2_filter.cd4$sum <- rowSums(prop.table.tra2_filter.cd4)
prop.table.tra2_filter.cd4 <- prop.table.tra2_filter.cd4 %>% filter(sum >0) %>% select(-sum)

prop.table.trb2_filter.cd4 <- prop.table.trb_filter[,1:14]
prop.table.trb2_filter.cd4$sum <- rowSums(prop.table.trb2_filter.cd4)
prop.table.trb2_filter.cd4 <- prop.table.trb2_filter.cd4 %>% filter(sum >0) %>% select(-sum)

prop.table.tra2_filter.cd8 <- prop.table.tra_filter[,15:28]
prop.table.tra2_filter.cd8$sum <- rowSums(prop.table.tra2_filter.cd8)
prop.table.tra2_filter.cd8 <- prop.table.tra2_filter.cd8 %>% filter(sum >0) %>% select(-sum)

prop.table.trb2_filter.cd8 <- prop.table.trb_filter[,15:28]
prop.table.trb2_filter.cd8$sum <- rowSums(prop.table.trb2_filter.cd8)
prop.table.trb2_filter.cd8 <- prop.table.trb2_filter.cd8 %>% filter(sum >0) %>% select(-sum)

PCA plots

CD4

prop.table.tra2_filter.merge <- rbind(prop.table.tra2_filter.cd4, prop.table.trb2_filter.cd4)
  
res.pca.merge.cd4 <- prcomp(t(prop.table.tra2_filter.merge), scale = TRUE, center = T)

mdres.pca.merge.cd4  <-  colnames(prop.table.tra2_filter.merge[,1:14])  %>% 
  as.data.frame() %>% 
  mutate(sample = stringr::str_replace_all(., pattern = "Lymph nodes", replacement = "LN")) %>% 
  separate(sample, into = c("Cell_type","Organ","Strain","Exp"))

fviz_pca_ind(res.pca.merge.cd4,
             col.ind = as.factor(mdres.pca.merge.cd4$Strain), # color by groups
             legend.title = "Groups",
             geom.ind = "point",
             invisible="quali", pointsize = 0) +
   scale_color_manual(values=c("dodgerblue","indianred2","gray40")) +
   geom_point(aes(shape = as.factor(mdres.pca.merge.cd4$Organ),
                  color = as.factor(mdres.pca.merge.cd4$Strain))) + 
  scale_shape_manual(values = c(8,8,8,15,15)) +
  ggtheme() +
  theme(axis.text.x = element_text(angle = 0))

#ggsave("final_fig/pca/cd4.png", width = 2.6, height = 1.7)
#ggsave("final_fig/pca/cd4.svg", width = 2.6, height = 1.7)

Zoom

fviz_pca_ind(res.pca.merge.cd4,
             col.ind = as.factor(mdres.pca.merge.cd4$Strain), # color by groups
             legend.title = "Groups",
             geom.ind = "point",
             invisible="quali", pointsize = 0) +
   scale_color_manual(values=c("dodgerblue","indianred2","gray40")) +
   geom_point(aes(shape = as.factor(mdres.pca.merge.cd4$Organ),
                  color = as.factor(mdres.pca.merge.cd4$Strain))) + 
  scale_shape_manual(values = c(8,8,8,15,15)) +
  xlim(c(-30,0)) + ylim(-2,15) +
  ggtheme() +
  theme(axis.text.x = element_text(angle = 0))

#ggsave("final_fig/pca/cd4_zoom.png", width = 2.2, height = 1.4)
#ggsave("final_fig/pca/cd4_zoom.svg", width = 2.2, height = 1.4)

CD8

prop.table.tra2_filter.merge <- rbind(prop.table.tra2_filter.cd8, prop.table.trb2_filter.cd8)
  
res.pca.merge.cd8 <- prcomp(t(prop.table.tra2_filter.merge), scale = TRUE, center = T)

mdres.pca.merge.cd8  <-  colnames(prop.table.tra2_filter.merge[,1:14])  %>% 
  as.data.frame() %>% 
  mutate(sample = stringr::str_replace_all(., pattern = "Lymph nodes", replacement = "LN")) %>% 
  separate(sample, into = c("Cell_type","Organ","Strain","Exp"))
  

fviz_pca_ind(res.pca.merge.cd8,
             col.ind = as.factor(mdres.pca.merge.cd8$Strain), # color by groups
             legend.title = "Groups",
             geom.ind = "point",
             invisible="quali", pointsize = 0) +
   scale_color_manual(values=c("dodgerblue","indianred2","gray40")) +
   geom_point(aes(shape = as.factor(mdres.pca.merge.cd8$Organ),
                  color = as.factor(mdres.pca.merge.cd8$Strain))) + 
  scale_shape_manual(values = c(8,8,8,15,15)) +
  ggtheme()

#ggsave("final_fig/pca/cd8.png", width = 2.6, height = 1.7)
#ggsave("final_fig/pca/cd8.svg", width = 2.6, height = 1.7)

Zoom

fviz_pca_ind(res.pca.merge.cd8,
             col.ind = as.factor(mdres.pca.merge.cd8$Strain), # color by groups
             legend.title = "Groups",
             geom.ind = "point",
             invisible="quali", pointsize = 0) +
   scale_color_manual(values=c("dodgerblue","indianred2","gray40")) +
   geom_point(aes(shape = as.factor(mdres.pca.merge.cd8$Organ),
                  color = as.factor(mdres.pca.merge.cd8$Strain))) + 
  scale_shape_manual(values = c(8,8,8,15,15)) +
  xlim(c(-32,-18)) + ylim(-4,5) +
  ggtheme()

#ggsave("final_fig/pca/cd8_zoom.png", width = 2.2, height = 1.4)
#ggsave("final_fig/pca/cd8_zoom.svg", width = 2.2, height = 1.4)

TRA

CD4

## LN
prop.table.tra2_filter.cd4.ln <- prop.table.tra_filter[,1:7]
prop.table.tra2_filter.cd4.ln$sum <- rowSums(prop.table.tra2_filter.cd4.ln)

prop.table.tra2_filter.cd4.ln <- prop.table.tra2_filter.cd4.ln %>% filter(sum >0) %>% select(-sum)

res.pca.tra.cd4.ln <- prcomp(t(prop.table.tra2_filter.cd4.ln), scale = TRUE, center = T)

mdres.pca.tra.cd4.ln  <-  colnames(prop.table.tra_filter[,1:7])  %>% 
  as.data.frame() %>% 
  mutate(sample = stringr::str_replace_all(., pattern = "Lymph nodes", replacement = "LN")) %>% 
  separate(sample, into = c("Cell_type","Organ","Strain","Exp"))
  

fviz_pca_ind(res.pca.tra.cd4.ln,
             col.ind = as.factor(mdres.pca.tra.cd4.ln$Strain), # color by groups
             legend.title = "Groups",
             geom.ind = "point",
             invisible="quali", pointsize = 0) +
   scale_color_manual(values=c("dodgerblue","indianred2","gray40")) +
   geom_point(aes(shape = as.factor(mdres.pca.tra.cd4.ln$Organ),
                  color = as.factor(mdres.pca.tra.cd4.ln$Strain))) +
  geom_hline(yintercept = 0, color = "grey20") +
  geom_vline(xintercept = 0, color = "grey20") 

#ggsave("final_fig/pca/cd4_tra_ln.png", width = 2.6, height = 1.7)
#ggsave("final_fig/pca/cd4_tra_ln.svg", width = 2.6, height = 1.7)


## Thymus
prop.table.tra2_filter.cd4.thy <- prop.table.tra_filter[,8:14]
prop.table.tra2_filter.cd4.thy$sum <- rowSums(prop.table.tra2_filter.cd4.thy)

prop.table.tra2_filter.cd4.thy <- prop.table.tra2_filter.cd4.thy %>% filter(sum >0) %>% select(-sum)

res.pca.tra.cd4.thy <- prcomp(t(prop.table.tra2_filter.cd4.thy), scale = TRUE, center = T)

mdres.pca.tra.cd4.thy  <-  colnames(prop.table.tra_filter[,8:14])  %>% 
  as.data.frame() %>% 
  mutate(sample = stringr::str_replace_all(., pattern = "Lymph nodes", replacement = "LN")) %>% 
  separate(sample, into = c("Cell_type","Organ","Strain","Exp"))
  
fviz_pca_ind(res.pca.tra.cd4.thy,
             col.ind = as.factor(mdres.pca.tra.cd4.thy$Strain), # color by groups
             legend.title = "Groups",
             geom.ind = "point",
             invisible="quali", pointsize = 0) +
   scale_color_manual(values=c("dodgerblue","indianred2","gray40")) +
   geom_point(aes(shape = as.factor(mdres.pca.tra.cd4.thy$Organ),
                  color = as.factor(mdres.pca.tra.cd4.thy$Strain))) +
  geom_hline(yintercept = 0, color = "grey20") +
  geom_vline(xintercept = 0, color = "grey20") +
  ggtheme()

#ggsave("final_fig/pca/cd4_tra_thy.png", width = 2.6, height = 1.7)
#ggsave("final_fig/pca/cd4_tra_thy.svg", width = 2.6, height = 1.7)

CD8

## All
prop.table.tra2_filter.cd8 <- prop.table.tra_filter[,15:28]
prop.table.tra2_filter.cd8$sum <- rowSums(prop.table.tra2_filter.cd8)

prop.table.tra2_filter.cd8 <- prop.table.tra2_filter.cd8 %>% filter(sum >0) %>% select(-sum)

res.pca.tra.cd8 <- prcomp(t(prop.table.tra2_filter.cd8), scale = TRUE, center = T)

mdres.pca.tra.cd8  <-  colnames(prop.table.tra2_filter[,15:28])  %>% 
  as.data.frame() %>% 
  mutate(sample = stringr::str_replace_all(., pattern = "Lymph nodes", replacement = "LN")) %>% 
  separate(sample, into = c("Cell_type","Organ","Strain","Exp"))
## Error in is.data.frame(x): object 'prop.table.tra2_filter' not found
## LN
prop.table.tra2_filter %>% colnames
## Error in is.data.frame(x): object 'prop.table.tra2_filter' not found
prop.table.tra2_filter.cd8.ln <- prop.table.tra_filter[,15:21]
prop.table.tra2_filter.cd8.ln$sum <- rowSums(prop.table.tra2_filter.cd8.ln)

prop.table.tra2_filter.cd8.ln <- prop.table.tra2_filter.cd8.ln %>% filter(sum >0) %>% select(-sum)

res.pca.tra.cd8.ln <- prcomp(t(prop.table.tra2_filter.cd8.ln), scale = TRUE, center = T)

mdres.pca.tra.cd8.ln  <-  colnames(prop.table.tra_filter[,15:21])  %>% 
  as.data.frame() %>% 
  mutate(sample = stringr::str_replace_all(., pattern = "Lymph nodes", replacement = "LN")) %>% 
  separate(sample, into = c("Cell_type","Organ","Strain","Exp"))
  
fviz_pca_ind(res.pca.tra.cd8.ln,
             col.ind = as.factor(mdres.pca.tra.cd8.ln$Strain), # color by groups
             legend.title = "Groups",
             geom.ind = "point",
             invisible="quali", pointsize = 0) +
   scale_color_manual(values=c("dodgerblue","indianred2","gray40")) +
   geom_point(aes(shape = as.factor(mdres.pca.tra.cd8.ln$Organ),
                  color = as.factor(mdres.pca.tra.cd8.ln$Strain))) +
  geom_hline(yintercept = 0, color = "grey20") +
  geom_vline(xintercept = 0, color = "grey20") 

#ggsave("final_fig/pca/cd8_tra_ln.png", width = 2.6, height = 1.7)
#ggsave("final_fig/pca/cd8_tra_ln.svg", width = 2.6, height = 1.7)

## Thymus
prop.table.tra2_filter %>% colnames
## Error in is.data.frame(x): object 'prop.table.tra2_filter' not found
prop.table.tra2_filter.cd8.thy <- prop.table.tra_filter[,22:28]
prop.table.tra2_filter.cd8.thy$sum <- rowSums(prop.table.tra2_filter.cd8.thy)

prop.table.tra2_filter.cd8.thy <- prop.table.tra2_filter.cd8.thy %>% filter(sum >0) %>% select(-sum)

res.pca.tra.cd8.thy <- prcomp(t(prop.table.tra2_filter.cd8.thy), scale = TRUE, center = T)

mdres.pca.tra.cd8.thy  <-  colnames(prop.table.tra_filter[,22:28])  %>% 
  as.data.frame() %>% 
  mutate(sample = stringr::str_replace_all(., pattern = "Lymph nodes", replacement = "LN")) %>% 
  separate(sample, into = c("Cell_type","Organ","Strain","Exp"))
 
fviz_pca_ind(res.pca.tra.cd8.thy,
             col.ind = as.factor(mdres.pca.tra.cd8.thy$Strain), # color by groups
             legend.title = "Groups",
             geom.ind = "point",
             invisible="quali", pointsize = 0) +
   scale_color_manual(values=c("dodgerblue","indianred2","gray40")) +
   geom_point(aes(shape = as.factor(mdres.pca.tra.cd8.thy$Organ),
                  color = as.factor(mdres.pca.tra.cd8.thy$Strain))) +
  geom_hline(yintercept = 0, color = "grey20") +
  geom_vline(xintercept = 0, color = "grey20") 

#ggsave("final_fig/pca/cd8_tra_thy.png", width = 2.6, height = 1.7)
#ggsave("final_fig/pca/cd8_tra_thy.svg", width = 2.6, height = 1.7)

TRB

res.pca.trb <- prcomp(t(prop.table.trb_filter[,5:32]), scale = TRUE)
## Error in colMeans(x, na.rm = TRUE): 'x' must be numeric
md_trb  <-  colnames(prop.table.trb_filter[,5:32])  %>% 
  as.data.frame() %>% 
  mutate(sample = stringr::str_replace_all(., pattern = "Lymph nodes", replacement = "LN")) %>% 
  separate(sample, into = c("Cell_type","Organ","Strain","Exp"))

CD4

## All
colnames(prop.table.trb2_filter)
## Error in is.data.frame(x): object 'prop.table.trb2_filter' not found
prop.table.trb2_filter.cd4 <- prop.table.trb_filter[,5:18]
prop.table.trb2_filter.cd4$sum <- rowSums(prop.table.trb2_filter.cd4)

prop.table.trb2_filter.cd4 <- prop.table.trb2_filter.cd4 %>% filter(sum >0) %>% select(-sum)

res.pca.trb.cd4 <- prcomp(t(prop.table.trb2_filter.cd4), scale = TRUE, center = T)

mdres.pca.trb.cd4  <-  colnames(prop.table.trb_filter[,5:18])  %>% 
  as.data.frame() %>% 
  mutate(sample = stringr::str_replace_all(., pattern = "Lymph nodes", replacement = "LN")) %>% 
  separate(sample, into = c("Cell_type","Organ","Strain","Exp"))
 

## LN
prop.table.trb_filter %>% colnames
##  [1] "CD4 Lymph nodes CA Exp01"   "CD4 Lymph nodes CA Exp02"  
##  [3] "CD4 Lymph nodes CA Exp03"   "CD4 Lymph nodes CAKR Exp02"
##  [5] "CD4 Lymph nodes CAKR Exp03" "CD4 Lymph nodes WT Exp02"  
##  [7] "CD4 Lymph nodes WT Exp03"   "CD4 Thymus CA Exp01"       
##  [9] "CD4 Thymus CA Exp03"        "CD4 Thymus CAKR Exp01"     
## [11] "CD4 Thymus CAKR Exp02"      "CD4 Thymus CAKR Exp03"     
## [13] "CD4 Thymus WT Exp02"        "CD4 Thymus WT Exp03"       
## [15] "CD8 Lymph nodes CA Exp02"   "CD8 Lymph nodes CA Exp03"  
## [17] "CD8 Lymph nodes CAKR Exp02" "CD8 Lymph nodes CAKR Exp03"
## [19] "CD8 Lymph nodes WT Exp01"   "CD8 Lymph nodes WT Exp02"  
## [21] "CD8 Lymph nodes WT Exp03"   "CD8 Thymus CA Exp01"       
## [23] "CD8 Thymus CA Exp03"        "CD8 Thymus CAKR Exp02"     
## [25] "CD8 Thymus CAKR Exp03"      "CD8 Thymus WT Exp01"       
## [27] "CD8 Thymus WT Exp02"        "CD8 Thymus WT Exp03"       
## [29] "aaSeqCDR3"                  "allVHitsWithScore"         
## [31] "allDHitsWithScore"          "allJHitsWithScore"
prop.table.trb2_filter.cd4.ln <- prop.table.trb_filter[,5:11]
prop.table.trb2_filter.cd4.ln$sum <- rowSums(prop.table.trb2_filter.cd4.ln)

prop.table.trb2_filter.cd4.ln <- prop.table.trb2_filter.cd4.ln %>% filter(sum >0) %>% select(-sum)

res.pca.trb.cd4.ln <- prcomp(t(prop.table.trb2_filter.cd4.ln), scale = TRUE, center = T)

mdres.pca.trb.cd4.ln  <-  colnames(prop.table.trb_filter[,5:11])  %>% 
  as.data.frame() %>% 
  mutate(sample = stringr::str_replace_all(., pattern = "Lymph nodes", replacement = "LN")) %>% 
  separate(sample, into = c("Cell_type","Organ","Strain","Exp"))
  

fviz_pca_ind(res.pca.trb.cd4.ln,
             col.ind = as.factor(mdres.pca.trb.cd4.ln$Strain), # color by groups
             legend.title = "Groups",
             geom.ind = "point",
             invisible="quali", pointsize = 0) +
   scale_color_manual(values=c("dodgerblue","indianred2","gray40")) +
   geom_point(aes(shape = as.factor(mdres.pca.trb.cd4.ln$Organ),
                  color = as.factor(mdres.pca.trb.cd4.ln$Strain))) +
  geom_hline(yintercept = 0, color = "grey20") +
  geom_vline(xintercept = 0, color = "grey20") 

#ggsave("final_fig/pca/cd4_trb_ln.png", width = 2.6, height = 1.7)
#ggsave("final_fig/pca/cd4_trb_ln.svg", width = 2.6, height = 1.7)

## Thymus
prop.table.trb_filter %>% colnames
##  [1] "CD4 Lymph nodes CA Exp01"   "CD4 Lymph nodes CA Exp02"  
##  [3] "CD4 Lymph nodes CA Exp03"   "CD4 Lymph nodes CAKR Exp02"
##  [5] "CD4 Lymph nodes CAKR Exp03" "CD4 Lymph nodes WT Exp02"  
##  [7] "CD4 Lymph nodes WT Exp03"   "CD4 Thymus CA Exp01"       
##  [9] "CD4 Thymus CA Exp03"        "CD4 Thymus CAKR Exp01"     
## [11] "CD4 Thymus CAKR Exp02"      "CD4 Thymus CAKR Exp03"     
## [13] "CD4 Thymus WT Exp02"        "CD4 Thymus WT Exp03"       
## [15] "CD8 Lymph nodes CA Exp02"   "CD8 Lymph nodes CA Exp03"  
## [17] "CD8 Lymph nodes CAKR Exp02" "CD8 Lymph nodes CAKR Exp03"
## [19] "CD8 Lymph nodes WT Exp01"   "CD8 Lymph nodes WT Exp02"  
## [21] "CD8 Lymph nodes WT Exp03"   "CD8 Thymus CA Exp01"       
## [23] "CD8 Thymus CA Exp03"        "CD8 Thymus CAKR Exp02"     
## [25] "CD8 Thymus CAKR Exp03"      "CD8 Thymus WT Exp01"       
## [27] "CD8 Thymus WT Exp02"        "CD8 Thymus WT Exp03"       
## [29] "aaSeqCDR3"                  "allVHitsWithScore"         
## [31] "allDHitsWithScore"          "allJHitsWithScore"
prop.table.trb2_filter.cd4.thy <- prop.table.trb_filter[,12:18]
prop.table.trb2_filter.cd4.thy$sum <- rowSums(prop.table.trb2_filter.cd4.thy)

prop.table.trb2_filter.cd4.thy <- prop.table.trb2_filter.cd4.thy %>% filter(sum >0) %>% select(-sum)

res.pca.trb.cd4.thy <- prcomp(t(prop.table.trb2_filter.cd4.thy), scale = TRUE, center = T)

mdres.pca.trb.cd4.thy  <-  colnames(prop.table.trb_filter[,12:18])  %>% 
  as.data.frame() %>% 
  mutate(sample = stringr::str_replace_all(., pattern = "Lymph nodes", replacement = "LN")) %>% 
  separate(sample, into = c("Cell_type","Organ","Strain","Exp"))
 
fviz_pca_ind(res.pca.trb.cd4.thy,
             col.ind = as.factor(mdres.pca.trb.cd4.thy$Strain), # color by groups
             legend.title = "Groups",
             geom.ind = "point",
             invisible="quali", pointsize = 0) +
   scale_color_manual(values=c("dodgerblue","indianred2","gray40")) +
   geom_point(aes(shape = as.factor(mdres.pca.trb.cd4.thy$Organ),
                  color = as.factor(mdres.pca.trb.cd4.thy$Strain))) +
  geom_hline(yintercept = 0, color = "grey20") +
  geom_vline(xintercept = 0, color = "grey20") 

#ggsave("final_fig/pca/cd4_trb_thy.png", width = 2.6, height = 1.7)
#ggsave("final_fig/pca/cd4_trb_thy.svg", width = 2.6, height = 1.7)

CD8

## All

prop.table.trb2_filter.cd8 <- prop.table.trb_filter[,19:32]
prop.table.trb2_filter.cd8$sum <- rowSums(prop.table.trb2_filter.cd8)
## Error in rowSums(prop.table.trb2_filter.cd8): 'x' must be numeric
prop.table.trb2_filter.cd8 <- prop.table.trb2_filter.cd8 %>% filter(sum >0) %>% select(-sum)
## Error in `filter()`:
## ! Problem while computing `..1 = sum > 0`.
## Caused by error in `sum > 0`:
## ! comparison (6) is possible only for atomic and list types
res.pca.trb.cd8 <- prcomp(t(prop.table.trb2_filter.cd8), scale = TRUE, center = T)
## Error in colMeans(x, na.rm = TRUE): 'x' must be numeric
mdres.pca.trb.cd8  <-  colnames(prop.table.trb_filter[,19:32])  %>% 
  as.data.frame() %>% 
  mutate(sample = stringr::str_replace_all(., pattern = "Lymph nodes", replacement = "LN")) %>% 
  separate(sample, into = c("Cell_type","Organ","Strain","Exp"))
  

## LN
prop.table.trb_filter %>% colnames
##  [1] "CD4 Lymph nodes CA Exp01"   "CD4 Lymph nodes CA Exp02"  
##  [3] "CD4 Lymph nodes CA Exp03"   "CD4 Lymph nodes CAKR Exp02"
##  [5] "CD4 Lymph nodes CAKR Exp03" "CD4 Lymph nodes WT Exp02"  
##  [7] "CD4 Lymph nodes WT Exp03"   "CD4 Thymus CA Exp01"       
##  [9] "CD4 Thymus CA Exp03"        "CD4 Thymus CAKR Exp01"     
## [11] "CD4 Thymus CAKR Exp02"      "CD4 Thymus CAKR Exp03"     
## [13] "CD4 Thymus WT Exp02"        "CD4 Thymus WT Exp03"       
## [15] "CD8 Lymph nodes CA Exp02"   "CD8 Lymph nodes CA Exp03"  
## [17] "CD8 Lymph nodes CAKR Exp02" "CD8 Lymph nodes CAKR Exp03"
## [19] "CD8 Lymph nodes WT Exp01"   "CD8 Lymph nodes WT Exp02"  
## [21] "CD8 Lymph nodes WT Exp03"   "CD8 Thymus CA Exp01"       
## [23] "CD8 Thymus CA Exp03"        "CD8 Thymus CAKR Exp02"     
## [25] "CD8 Thymus CAKR Exp03"      "CD8 Thymus WT Exp01"       
## [27] "CD8 Thymus WT Exp02"        "CD8 Thymus WT Exp03"       
## [29] "aaSeqCDR3"                  "allVHitsWithScore"         
## [31] "allDHitsWithScore"          "allJHitsWithScore"
prop.table.trb2_filter.cd8.ln <- prop.table.trb_filter[,19:25]
prop.table.trb2_filter.cd8.ln$sum <- rowSums(prop.table.trb2_filter.cd8.ln)

prop.table.trb2_filter.cd8.ln <- prop.table.trb2_filter.cd8.ln %>% filter(sum >0) %>% select(-sum)

res.pca.trb.cd8.ln <- prcomp(t(prop.table.trb2_filter.cd8.ln), scale = TRUE, center = T)

mdres.pca.trb.cd8.ln  <-  colnames(prop.table.trb_filter[,19:25])  %>% 
  as.data.frame() %>% 
  mutate(sample = stringr::str_replace_all(., pattern = "Lymph nodes", replacement = "LN")) %>% 
  separate(sample, into = c("Cell_type","Organ","Strain","Exp"))
  
fviz_pca_ind(res.pca.trb.cd8.ln,
             col.ind = as.factor(mdres.pca.trb.cd8.ln$Strain), # color by groups
             legend.title = "Groups",
             geom.ind = "point",
             invisible="quali", pointsize = 0) +
   scale_color_manual(values=c("dodgerblue","indianred2","gray40")) +
   geom_point(aes(shape = as.factor(mdres.pca.trb.cd8.ln$Organ),
                  color = as.factor(mdres.pca.trb.cd8.ln$Strain))) +
  geom_hline(yintercept = 0, color = "grey20") +
  geom_vline(xintercept = 0, color = "grey20") 

#ggsave("final_fig/pca/cd8_trb_ln.png", width = 2.6, height = 1.7)
#ggsave("final_fig/pca/cd8_trb_ln.svg", width = 2.6, height = 1.7)


## Thymus
prop.table.trb_filter %>% colnames
##  [1] "CD4 Lymph nodes CA Exp01"   "CD4 Lymph nodes CA Exp02"  
##  [3] "CD4 Lymph nodes CA Exp03"   "CD4 Lymph nodes CAKR Exp02"
##  [5] "CD4 Lymph nodes CAKR Exp03" "CD4 Lymph nodes WT Exp02"  
##  [7] "CD4 Lymph nodes WT Exp03"   "CD4 Thymus CA Exp01"       
##  [9] "CD4 Thymus CA Exp03"        "CD4 Thymus CAKR Exp01"     
## [11] "CD4 Thymus CAKR Exp02"      "CD4 Thymus CAKR Exp03"     
## [13] "CD4 Thymus WT Exp02"        "CD4 Thymus WT Exp03"       
## [15] "CD8 Lymph nodes CA Exp02"   "CD8 Lymph nodes CA Exp03"  
## [17] "CD8 Lymph nodes CAKR Exp02" "CD8 Lymph nodes CAKR Exp03"
## [19] "CD8 Lymph nodes WT Exp01"   "CD8 Lymph nodes WT Exp02"  
## [21] "CD8 Lymph nodes WT Exp03"   "CD8 Thymus CA Exp01"       
## [23] "CD8 Thymus CA Exp03"        "CD8 Thymus CAKR Exp02"     
## [25] "CD8 Thymus CAKR Exp03"      "CD8 Thymus WT Exp01"       
## [27] "CD8 Thymus WT Exp02"        "CD8 Thymus WT Exp03"       
## [29] "aaSeqCDR3"                  "allVHitsWithScore"         
## [31] "allDHitsWithScore"          "allJHitsWithScore"
prop.table.trb2_filter.cd8.thy <- prop.table.trb_filter[,26:32]
prop.table.trb2_filter.cd8.thy$sum <- rowSums(prop.table.trb2_filter.cd8.thy)
## Error in rowSums(prop.table.trb2_filter.cd8.thy): 'x' must be numeric
prop.table.trb2_filter.cd8.thy <- prop.table.trb2_filter.cd8.thy %>% filter(sum >0) %>% select(-sum)
## Error in `filter()`:
## ! Problem while computing `..1 = sum > 0`.
## Caused by error in `sum > 0`:
## ! comparison (6) is possible only for atomic and list types
res.pca.trb.cd8.thy <- prcomp(t(prop.table.trb2_filter.cd8.thy), scale = TRUE, center = T)
## Error in colMeans(x, na.rm = TRUE): 'x' must be numeric
mdres.pca.trb.cd8.thy  <-  colnames(prop.table.trb_filter[,26:32])  %>% 
  as.data.frame() %>% 
  mutate(sample = stringr::str_replace_all(., pattern = "Lymph nodes", replacement = "LN")) %>% 
  separate(sample, into = c("Cell_type","Organ","Strain","Exp"))
 
fviz_pca_ind(res.pca.trb.cd8.thy,
             col.ind = as.factor(mdres.pca.trb.cd8.thy$Strain), # color by groups
             legend.title = "Groups",
             geom.ind = "point",
             invisible="quali", pointsize = 0) +
   scale_color_manual(values=c("dodgerblue","indianred2","gray40")) +
   geom_point(aes(shape = as.factor(mdres.pca.trb.cd8.thy$Organ),
                  color = as.factor(mdres.pca.trb.cd8.thy$Strain))) +
  geom_hline(yintercept = 0, color = "grey20") +
  geom_vline(xintercept = 0, color = "grey20") 
## Error in .get_facto_class(X): object 'res.pca.trb.cd8.thy' not found
#ggsave("final_fig/pca/cd8_trb_thy.png", width = 2.6, height = 1.7)
#ggsave("final_fig/pca/cd8_trb_thy.svg", width = 2.6, height = 1.7)

Diversity index

md <- read.csv("metadata_Lck.csv")
immdata_tra_wonkt <- repFilter(immdata_tra, "by.clonotype",
  list(V.name = exclude("TRAV11"), J.name = exclude("TRAJ18")),
  .match = "substring")

## ALL TRA
repDiversity(immdata_tra_wonkt$data) %>% vis(.by = c( "Mouse_strain"), .meta = immdata_tra$meta, 
  .signif.label.size = 0) + ggtitle("TRA without NKT") + scale_fill_manual(values = c("dodgerblue","indianred2","gray70")) + ggtitle("all TRA", subtitle = "")

#ggsave("./final_fig/diversity/chao_tra_wonkt_sign.png", width = 8, height = 13, units = "cm")
#ggsave("./final_fig/diversity/chao_tra_wonkt_sign.svg", width = 8, height = 13, units = "cm")


## ALL TRB
repDiversity(immdata_trb$data) %>% vis(.by = c( "Mouse_strain"), .meta = immdata_trb$meta, 
  .signif.label.size = 0) + ggtitle("TRB without NKT") + scale_fill_manual(values = c("dodgerblue","indianred2","gray70")) + ggtitle("all TRB", subtitle = "")

#ggsave("./final_fig/diversity/chao_trb_sign.png", width = 8, height = 13, units = "cm")
#ggsave("./final_fig/diversity/chao_trb_sign.svg", width = 8, height = 13, units = "cm")


## Chao index
repDiversity(immdata_tra_wonkt$data) %>% vis(.by = c("Cell_type", "Organ","Mouse_strain"), .meta = immdata_tra$meta, .test = F,
  .signif.label.size = 0) + ggtitle("TRA")

#ggsave("./plots/chao_tra.png", width = 15, height = 11, units = "cm")
#ggsave("./plots/chao_tra.svg", width = 15, height = 11, units = "cm")

repDiversity(immdata_trb$data) %>% vis(.by = c("Cell_type", "Organ","Mouse_strain"), .meta = immdata_trb$meta, .test = F,
  .signif.label.size = 0) + ggtitle("TRB", subtitle = "")

#ggsave("./plots/chao_trb.png", width = 15, height = 11, units = "cm")
#ggsave("./plots/chao_trb.svg", width = 15, height = 11, units = "cm")
chao_tra <- as.data.frame(repDiversity(immdata_tra_wonkt$data))
chao_trb <- as.data.frame(repDiversity(immdata_trb$data))
chao_tra2 <- chao_tra %>% 
  rownames_to_column("num_id") %>% 
mutate(num_id = str_replace_all(string = num_id, c("new_lib_24" = "lib24_S24_L001",  "new_lib_25" = "lib25_S25_L001"))) %>% 
  separate(num_id, into = c("num_id",NA,NA), sep = "_") %>% 
  mutate(num_id = str_replace(num_id, "lib","")) %>% 
  mutate_at(vars("num_id"), as.numeric) 
  

levels_cd4 <- c("CD4 Thymus WT", "CD4 Thymus CA", "CD4 Thymus CAKR", "CD4 Lymph nodes WT", "CD4 Lymph nodes CA", "CD4 Lymph nodes CAKR")

# Attach metadata sent for sequencing

chao_tra2 <- left_join(chao_tra2, md) %>% select(value = Estimator, Cell_type, Organ, Mouse_strain) %>%
   mutate(sample_type = paste(Cell_type, Organ, Mouse_strain)) 

chao_tra2 %>% filter(Cell_type == "CD4") %>% 
  ggplot(aes(y = value, x = factor(sample_type, levels = levels_cd4))) +
  stat_summary(fun = "mean",
               geom = "crossbar", 
               width = 0.5,
               colour = "black") +
  geom_point(aes(color = Mouse_strain)) + 
  ylab("Chao index estimate") + 
  ggtitle("CD4 TRA") + 
  theme(axis.text.x = element_text(angle = 90)) + 
  theme_classic() + 
  ggtheme() + 
  theme(axis.text.x = element_text(angle = 90)) +
  scale_color_manual(values = c("dodgerblue","indianred2","gray40")) + 
  ylim(c(0,NA)) + xlab("")

#ggsave("./final_fig/diversity/chao_cd4_tra.png", width = 8, height = 10, units = "cm")
#ggsave("./final_fig/diversity/chao_cd4_tra.eps", width = 8, height = 10, units = "cm")

levels_cd8 <- c("CD8 Thymus WT", "CD8 Thymus CA", "CD8 Thymus CAKR", "CD8 Lymph nodes WT", "CD8 Lymph nodes CA", "CD8 Lymph nodes CAKR")

chao_tra2 %>% filter(Cell_type == "CD8") %>% 
  ggplot(aes(y = value, x = factor(sample_type, levels = levels_cd8))) +
  stat_summary(fun = "mean",
               geom = "crossbar", 
               width = 0.5,
               colour = "black") +
  geom_point(aes(color = Mouse_strain)) + 
  ylab("Chao index estimate") + 
  ggtitle("CD8 TRA") + 
  theme(axis.text.x = element_text(angle = 90)) + 
  theme_classic() + 
  ggtheme() + 
  theme(axis.text.x = element_text(angle = 90)) +
  scale_color_manual(values = c("dodgerblue","indianred2","gray40")) + 
  ylim(c(0,NA)) + xlab("")

#ggsave("./final_fig/diversity/chao_cd8_tra.png", width = 8, height = 10, units = "cm")
#ggsave("./final_fig/diversity/chao_cd8_tra.eps", width = 8, height = 10, units = "cm")
chao_trb2 <- chao_trb %>% 
  rownames_to_column("num_id") %>% mutate(num_id = str_replace_all(string = num_id, c("new_lib_24" = "lib24_S24_L001",  "new_lib_25" = "lib25_S25_L001"))) %>% 
  separate(num_id, into = c("num_id",NA,NA), sep = "_") %>% 
  mutate(num_id = str_replace(num_id, "lib",""))  %>% 
  mutate_at(vars("num_id"), as.numeric) 
  

levels_cd4 <- c("CD4 Thymus WT", "CD4 Thymus CA", "CD4 Thymus CAKR", "CD4 Lymph nodes WT", "CD4 Lymph nodes CA", "CD4 Lymph nodes CAKR")

chao_trb2 <- left_join(chao_trb2, md) %>% select(value = Estimator, Cell_type, Organ, Mouse_strain) %>%
   mutate(sample_type = paste(Cell_type, Organ, Mouse_strain)) 

chao_trb2 %>% filter(Cell_type == "CD4") %>% 
  ggplot(aes(y = value, x = factor(sample_type, levels = levels_cd4))) +
  stat_summary(fun = "mean",
               geom = "crossbar", 
               width = 0.5,
               colour = "black") +
  geom_point(aes(color = Mouse_strain)) + 
  ylab("Chao index estimate") + 
  ggtitle("CD4 TRB") + 
  theme(axis.text.x = element_text(angle = 90)) + 
  theme_classic() + 
  ggtheme() + 
  theme(axis.text.x = element_text(angle = 90)) +
  scale_color_manual(values = c("dodgerblue","indianred2","gray40")) + 
  ylim(c(0,125500)) + xlab("")

#ggsave("./final_fig/diversity/chao_cd4_trb.png", width = 8, height = 10, units = "cm")
#ggsave("./final_fig/diversity/chao_cd4_trb.eps", width = 8, height = 10, units = "cm")

levels_cd8 <- c("CD8 Thymus WT", "CD8 Thymus CA", "CD8 Thymus CAKR", "CD8 Lymph nodes WT", "CD8 Lymph nodes CA", "CD8 Lymph nodes CAKR")


chao_trb2 %>% filter(Cell_type == "CD8") %>% 
  ggplot(aes(y = value, x = factor(sample_type, levels = levels_cd8))) +
  stat_summary(fun = "mean",
               geom = "crossbar", 
               width = 0.5,
               colour = "black") +
  geom_point(aes(color = Mouse_strain)) + 
  ylab("Chao index estimate") + 
  ggtitle("CD8 TRB") + 
  theme(axis.text.x = element_text(angle = 90)) + 
  theme_classic() + 
  ggtheme() + 
  theme(axis.text.x = element_text(angle = 90)) +
  scale_color_manual(values = c("dodgerblue","indianred2","gray40")) + 
  ylim(c(0,125500)) + xlab("")

#ggsave("./final_fig/diversity/chao_cd8_trb.png", width = 8, height = 10, units = "cm")
#ggsave("./final_fig/diversity/chao_cd8_trb.eps", width = 8, height = 10, units = "cm")
chao_trb2 %>% 
  ggplot(aes(y = value, x = factor(Mouse_strain, levels = c("WT","CA","CAKR")))) +
  stat_summary(fun = "mean",
               geom = "crossbar", 
               width = 0.5,
               colour = "black") +
  geom_dotplot(binaxis='y', stackdir='center', dotsize=0) + 
  geom_jitter(position=position_jitter(0.1), size = 2, aes(color = Mouse_strain)) +
ylab("Chao index estimate") + 
  theme(axis.text.x = element_text(angle = 90)) + 
  theme_classic() + 
  ggtheme() + 
  theme(axis.text.x = element_text(angle = 90)) +
  scale_color_manual(values = c("dodgerblue","indianred2","gray40")) + 
  ylim(c(0,NA)) + xlab("")

#ggsave("./final_fig/diversity/chao_all_trb.png", width = 8, height = 6, units = "cm")
#ggsave("./final_fig/diversity/chao_all_trb.eps", width = 8, height = 6, units = "cm")


chao_tra2 %>% 
  ggplot(aes(y = value, x = factor(Mouse_strain, levels = c("WT","CA","CAKR")))) +
  stat_summary(fun = "mean",
               geom = "crossbar", 
               width = 0.5,
               colour = "black") +
  geom_dotplot(binaxis='y', stackdir='center', dotsize=0) + 
  geom_jitter(position=position_jitter(0.1), size = 2, aes(color = Mouse_strain)) +
ylab("Chao index estimate") + 
  theme(axis.text.x = element_text(angle = 90)) + 
  theme_classic() + 
  ggtheme() + 
  theme(axis.text.x = element_text(angle = 90)) +
  scale_color_manual(values = c("dodgerblue","indianred2","gray40")) + 
  ylim(c(0,NA)) + xlab("")

#ggsave("./final_fig/diversity/chao_all_tra.png", width = 8, height = 6, units = "cm")
#ggsave("./final_fig/diversity/chao_all_tra.eps", width = 8, height = 6, units = "cm")

Top20 WT LN sequences

In the next analyses, we will figure out whether the sequences that are abundant in the periphery of WT animals are also present in LckKO animals. We will select the top 20 most abundant sequences from the lymph nodes of WT animals by calculating the average normalized frequency:

## TRA
topclones <- prop.table.tra_filter %>% 
  mutate(topclones_ln_cd4 = (`CD4 Lymph nodes WT Exp02`+
                               `CD4 Lymph nodes WT Exp03`)/2,
         topclones_ln_cd8 = (`CD8 Lymph nodes WT Exp01`+
                               `CD8 Lymph nodes WT Exp02`+
                               `CD8 Lymph nodes WT Exp03`)/3)

cd4_topclones_20sequences_tra <-
  pull(topclones %>% slice_max(order_by = topclones_ln_cd4, n = 20),
       aaSeqCDR3)
cd8_topclones_20sequences_tra <-
  pull(topclones %>% slice_max(order_by = topclones_ln_cd8, n = 20),
       aaSeqCDR3)

## TRB
topclones <- prop.table.trb_filter %>% 
  mutate(topclones_ln_cd4 = (`CD4 Lymph nodes WT Exp02`+
                               `CD4 Lymph nodes WT Exp03`)/2,
         topclones_ln_cd8 = (`CD8 Lymph nodes WT Exp01`+
                               `CD8 Lymph nodes WT Exp02`+
                               `CD8 Lymph nodes WT Exp03`)/3)

cd4_topclones_20sequences_trb <- pull(topclones %>% 
                                        slice_max(order_by = topclones_ln_cd4, n = 20), aaSeqCDR3)
cd8_topclones_20sequences_trb <- pull(topclones %>% 
                                        slice_max(order_by = topclones_ln_cd8, n = 20), aaSeqCDR3)

Top 20 most abundant TRA sequences in CD4 WT LNs:

cd4_topclones_20sequences_tra
##  [1] "CAASANSGTYQRF"  "CAASASSGSWQLIF" "CAAASSGSWQLIF"  "CAASDYSNNRLTL" 
##  [5] "CAASDTNTGKLTF"  "CAASRNSNNRIFF"  "CAASDQGGRALIF"  "CAASNTNTGKLTF" 
##  [9] "CAASPNTNKVVF"   "CAASSNTNKVVF"   "CAAGTGGYKVVF"   "CAASITGNTGKLIF"
## [13] "CAASDTNAYKVIF"  "CAASSSGSWQLIF"  "CAATGNTGKLIF"   "CAASGGSNAKLTF" 
## [17] "CAASGTGGYKVVF"  "CAASSGSWQLIF"   "CAARSSGSWQLIF"  "CAARNSNNRIFF"

Top 20 most abundant TRB sequences in CD4 WT LNs:

cd4_topclones_20sequences_trb
##  [1] "CASSLGQNTEVFF"  "CASSGTANSDYTF"  "CASSLDSQNTLYF"  "CASSRDNYAEQFF" 
##  [5] "CASSQGQGSYEQYF" "CASSPTGVQDTQYF" "CAWSLGGQDTQYF"  "CASSRDWGYEQYF" 
##  [9] "CASSRDSYAEQFF"  "CASSRDSQNTLYF"  "CASSLGGQNTLYF"  "CASSLGSQNTLYF" 
## [13] "CASSLSQNTLYF"   "CASSLQGNTEVFF"  "CASGDEQYF"      "CASGDAGGQNTLYF"
## [17] "CASSGTANTEVFF"  "CASSQEGGGTEVFF" "CASSRQGNTEVFF"  "CASSPSSYEQYF"

Top 20 most abundant TRA sequences in CD8 WT LNs:

cd8_topclones_20sequences_tra
##  [1] "CAASASSGSWQLIF"  "CALSDRYNQGKLIF"  "CAASNMGYKLTF"    "CAVSASSGSWQLIF" 
##  [5] "CAVDTNAYKVIF"    "CAAASSGSWQLIF"   "CAASSGSWQLIF"    "CAASDNYAQGLTF"  
##  [9] "CAASDTNAYKVIF"   "CAADTNAYKVIF"    "CAVSNMGYKLTF"    "CAASGTGGYKVVF"  
## [13] "CAAGATGGNNKLTF"  "CALMGYKLTF"      "CAASANSGTYQRF"   "CALGDTNAYKVIF"  
## [17] "CAASDDTNAYKVIF"  "CAASDMGYKLTF"    "CAASAGTGGYKVVF"  "CAMREGSSGSWQLIF"

Top 20 most abundant TRB sequences in CD8 WT LNs:

cd8_topclones_20sequences_trb
##  [1] "CASSDAGYEQYF"    "CASSPGTGGYEQYF"  "CASGDAGEQYF"     "CASGDAGGSAETLYF"
##  [5] "CASSDSAETLYF"    "CASSRDNYAEQFF"   "CASSPGQQDTQYF"   "CASSLGYEQYF"    
##  [9] "CASSLGAEQFF"     "CASSPGQYAEQFF"   "CASSLGGNYAEQFF"  "CASSDWGNYAEQFF" 
## [13] "CASSLGANSDYTF"   "CASSPGQNYAEQFF"  "CASSLGGQDTQYF"   "CASGDAGYEQYF"   
## [17] "CASSLGNYAEQFF"   "CASSRDNYEQYF"    "CASSDSYEQYF"     "CASSLDNYAEQFF"

Summary plots - percentage of the repertoire

Now we will investigate what is the percentage of the repertoire of each sample that is occupied by the top20 WT LN sequences that we identified earlier. We will plot the results.

CD4 TRA

cd4_topclones_rep_pct <- prop.table.tra2 %>% 
  filter(aaSeqCDR3 %in% cd4_topclones_20sequences_tra) %>% 
  select(aaSeqCDR3, starts_with("CD4")) %>% 
  select(1,14,15,9:13,7,8,2:6) 

cd4_topclones_rep_pct2 <- as.data.frame(colSums(cd4_topclones_rep_pct[,2:15]))
colnames(cd4_topclones_rep_pct2) <- "value"

cd4_topclones_rep_pct2 <- cd4_topclones_rep_pct2 %>% 
  rownames_to_column("sample") %>% 
  mutate(sample = stringr::str_replace_all(sample, pattern = "Lymph nodes", replacement = "LN")) %>% 
  separate(sample, into = c("Cell_type","Organ","Strain","Exp")) %>% 
  mutate(Cell_type_organ = factor(paste(Organ, Strain), 
                                  levels = c("Thymus WT", "Thymus CA", "Thymus CAKR",
                                             "LN WT", "LN CA", "LN CAKR")))

cd4_topclones_rep_pct2 %>% 
  filter(Organ == "LN") %>% 
  ggplot(aes(y = value*100, x = factor(Cell_type_organ))) +
  stat_summary(fun = "mean",
               geom = "crossbar", 
               width = 0.5,
               colour = "black") +
  geom_point(aes(color = Strain)) + 
  ylab("% of repertoire") + 
  ggtitle("CD4 TRA") + 
  theme(axis.text.x = element_text(angle = 90)) + 
  theme_classic() + 
  ggtheme() + 
  theme(axis.text.x = element_text(angle = 90)) +
  scale_color_manual(values = c("dodgerblue","indianred2","gray40")) + 
  ylim(c(0,NA)) + xlab("")

#ggsave("./final_fig/20topseqs/20topseqs_pct_tra_cd4.png", width = 5, height = 5.7, units = "cm")
#ggsave("./final_fig/20topseqs/20topseqs_pct_tra_cd4.eps", width = 5, height = 5.7, units = "cm")

CD4 TRB

cd4_topclones_rep_pct <- prop.table.trb2 %>% 
  filter(aaSeqCDR3 %in% cd4_topclones_20sequences_trb) %>% 
  select(aaSeqCDR3, starts_with("CD4")) %>% 
  select(1,14,15,9:13,7,8,2:6) 

cd4_topclones_rep_pct2 <- as.data.frame(colSums(cd4_topclones_rep_pct[,2:15]))
colnames(cd4_topclones_rep_pct2) <- "value"

cd4_topclones_rep_pct2 <- cd4_topclones_rep_pct2 %>% 
  rownames_to_column("sample") %>% 
  mutate(sample = stringr::str_replace_all(sample, pattern = "Lymph nodes", replacement = "LN")) %>% 
  separate(sample, into = c("Cell_type","Organ","Strain","Exp")) %>% 
  mutate(Cell_type_organ = factor(paste(Organ, Strain), levels = c("Thymus WT","Thymus CA", "Thymus CAKR",
                                                                   "LN WT","LN CA", "LN CAKR" )))

cd4_topclones_rep_pct2 %>% 
  filter(Organ == "LN") %>% 
  ggplot(aes(y = value*100, x = factor(Cell_type_organ))) +
  stat_summary(fun = "mean",
               geom = "crossbar", 
               width = 0.5,
               colour = "black") +
  geom_point(aes(color = Strain)) + 
  ylab("% of repertoire") + 
  ggtitle("CD4 TRB") + 
  theme(axis.text.x = element_text(angle = 90)) + 
  theme_classic() + 
  ggtheme() + 
  theme(axis.text.x = element_text(angle = 90)) +
  scale_color_manual(values = c("dodgerblue","indianred2","gray40")) + 
  ylim(c(0,NA)) + xlab("")

#ggsave("./final_fig/20topseqs/20topseqs_pct_trb_cd4.png", width = 5.4, height = 5.7, units = "cm")
#ggsave("./final_fig/20topseqs/20topseqs_pct_trb_cd4.eps", width = 5.4, height = 5.7, units = "cm")

CD8 TRA

cd8_topclones_rep_pct <- prop.table.tra2 %>% 
  filter(aaSeqCDR3 %in% cd8_topclones_20sequences_tra) %>% 
  select(aaSeqCDR3, starts_with("CD8")) %>% 
  select(1,13:15,9:12,6:8,2:5) 

cd8_topclones_rep_pct2 <- as.data.frame(colSums(cd8_topclones_rep_pct[,2:15]))
colnames(cd8_topclones_rep_pct2) <- "value"

cd8_topclones_rep_pct2 <- cd8_topclones_rep_pct2 %>% 
  rownames_to_column("sample") %>% 
  mutate(sample = stringr::str_replace_all(sample, pattern = "Lymph nodes", replacement = "LN")) %>% 
  separate(sample, into = c("Cell_type","Organ","Strain","Exp")) %>% 
  mutate(Cell_type_organ = factor(paste(Organ, Strain), levels = c("Thymus WT","Thymus CA", "Thymus CAKR",
                                                                   "LN WT","LN CA", "LN CAKR" )))

cd8_topclones_rep_pct2
##    Cell_type  Organ Strain   Exp      value Cell_type_organ
## 1        CD8 Thymus     WT Exp01 0.03400861       Thymus WT
## 2        CD8 Thymus     WT Exp02 0.02653846       Thymus WT
## 3        CD8 Thymus     WT Exp03 0.03137384       Thymus WT
## 4        CD8 Thymus     CA Exp01 0.02447936       Thymus CA
## 5        CD8 Thymus     CA Exp03 0.02870174       Thymus CA
## 6        CD8 Thymus   CAKR Exp02 0.03063281     Thymus CAKR
## 7        CD8 Thymus   CAKR Exp03 0.02280437     Thymus CAKR
## 8        CD8     LN     WT Exp01 0.03753115           LN WT
## 9        CD8     LN     WT Exp02 0.03764212           LN WT
## 10       CD8     LN     WT Exp03 0.03642344           LN WT
## 11       CD8     LN     CA Exp02 0.03592586           LN CA
## 12       CD8     LN     CA Exp03 0.03213014           LN CA
## 13       CD8     LN   CAKR Exp02 0.02946860         LN CAKR
## 14       CD8     LN   CAKR Exp03 0.03059997         LN CAKR
cd8_topclones_rep_pct2 %>% 
  filter(Organ == "LN") %>% 
  ggplot(aes(y = value*100, x = factor(Cell_type_organ))) +
  stat_summary(fun = "mean",
               geom = "crossbar", 
               width = 0.5,
               colour = "black") +
  geom_point(aes(color = Strain)) + 
  ylab("% of repertoire") + 
  ggtitle("CD8 TRA") + 
  theme(axis.text.x = element_text(angle = 90)) + 
  theme_classic() + 
  ggtheme() + 
  theme(axis.text.x = element_text(angle = 90)) +
  scale_color_manual(values = c("dodgerblue","indianred2","gray40")) + 
  ylim(c(0,NA)) + xlab("")

#ggsave("./final_fig/20topseqs/20topseqs_pct_tra_cd8.png", width = 5, height = 5.7, units = "cm")
#ggsave("./final_fig/20topseqs/20topseqs_pct_tra_cd8.eps", width = 5, height = 5.7, units = "cm")

CD8 TRB

cd8_topclones_rep_pct <- prop.table.trb2 %>% 
  filter(aaSeqCDR3 %in% cd8_topclones_20sequences_trb) %>% 
  select(aaSeqCDR3, starts_with("CD8")) %>% 
  select(1,13:15,9:12,6:8,2:5) 

cd8_topclones_rep_pct2 <- as.data.frame(colSums(cd8_topclones_rep_pct[,2:15]))
colnames(cd8_topclones_rep_pct2) <- "value"

cd8_topclones_rep_pct2 <- cd8_topclones_rep_pct2 %>% 
  rownames_to_column("sample") %>% 
  mutate(sample = stringr::str_replace_all(sample, pattern = "Lymph nodes", replacement = "LN")) %>% 
  separate(sample, into = c("Cell_type","Organ","Strain","Exp")) %>% 
  mutate(Cell_type_organ = factor(paste(Organ, Strain), levels = c("Thymus WT","Thymus CA", "Thymus CAKR",
                                                                   "LN WT","LN CA", "LN CAKR" )))

cd8_topclones_rep_pct2
##    Cell_type  Organ Strain   Exp       value Cell_type_organ
## 1        CD8 Thymus     WT Exp01 0.004631440       Thymus WT
## 2        CD8 Thymus     WT Exp02 0.004479484       Thymus WT
## 3        CD8 Thymus     WT Exp03 0.004929262       Thymus WT
## 4        CD8 Thymus     CA Exp01 0.005472183       Thymus CA
## 5        CD8 Thymus     CA Exp03 0.004517467       Thymus CA
## 6        CD8 Thymus   CAKR Exp02 0.003983029     Thymus CAKR
## 7        CD8 Thymus   CAKR Exp03 0.005094131     Thymus CAKR
## 8        CD8     LN     WT Exp01 0.007470700           LN WT
## 9        CD8     LN     WT Exp02 0.006188663           LN WT
## 10       CD8     LN     WT Exp03 0.007226656           LN WT
## 11       CD8     LN     CA Exp02 0.006775749           LN CA
## 12       CD8     LN     CA Exp03 0.007135684           LN CA
## 13       CD8     LN   CAKR Exp02 0.007553967         LN CAKR
## 14       CD8     LN   CAKR Exp03 0.006905450         LN CAKR
cd8_topclones_rep_pct2 %>% 
  filter(Organ == "LN") %>% 
  ggplot(aes(y = value*100, x = factor(Cell_type_organ))) +
  stat_summary(fun = "mean",
               geom = "crossbar", 
               width = 0.5,
               colour = "black") +
  geom_point(aes(color = Strain)) + 
  ylab("% of repertoire") + 
  ggtitle("CD8 TRB") + 
  theme(axis.text.x = element_text(angle = 90)) + 
  theme_classic() + 
  ggtheme() + 
  theme(axis.text.x = element_text(angle = 90)) +
  scale_color_manual(values = c("dodgerblue","indianred2","gray40")) + 
  ylim(c(0,NA)) + xlab("")

#ggsave("./final_fig/20topseqs/20topseqs_pct_trb_cd8.png", width = 5.4, height = 5.7, units = "cm")
#ggsave("./final_fig/20topseqs/20topseqs_pct_trb_cd8.eps", width = 5.4, height = 5.7, units = "cm")

Heatmaps

TRA

CD4

# TRA
cd4_topclones_heatmap_matrix <- prop.table.tra2 %>% 
  filter(aaSeqCDR3 %in% cd4_topclones_20sequences_tra) %>% 
  select(aaSeqCDR3, starts_with("CD4")) %>% 
  select(1,14,15,9:13,7,8,2:6)

cd4_topclones_heatmap_matrix2 <- as.matrix(cd4_topclones_heatmap_matrix[,2:15])
rownames(cd4_topclones_heatmap_matrix2) <- cd4_topclones_heatmap_matrix$aaSeqCDR3

cd4_topclones_heatmap_matrix2 <- cd4_topclones_heatmap_matrix2[match(cd4_topclones_20sequences_tra, rownames(cd4_topclones_heatmap_matrix2)),]

pheatmap(cd4_topclones_heatmap_matrix2, scale = "none", cluster_rows = F, cluster_cols = F)

#ggsave(plot = pheatmap(cd4_topclones_heatmap_matrix2, scale = "none", cluster_rows = F, cluster_cols = F), "final_fig/heatmap_top20_ordered/cd4_tra_noscale.svg")

CD8

# TRA

cd8_topclones_heatmap_matrix <- prop.table.tra2 %>% 
  filter(aaSeqCDR3 %in% cd8_topclones_20sequences_tra) %>% 
  select(aaSeqCDR3, starts_with("CD8")) %>% 
  select(1,13:15,9:12,6:8,2:5)
  

cd8_topclones_heatmap_matrix2 <- as.matrix(cd8_topclones_heatmap_matrix[,2:15])
rownames(cd8_topclones_heatmap_matrix2) <- cd8_topclones_heatmap_matrix$aaSeqCDR3

cd8_topclones_heatmap_matrix2 <- cd8_topclones_heatmap_matrix2[match(cd8_topclones_20sequences_tra, rownames(cd8_topclones_heatmap_matrix2)),]

pheatmap(cd8_topclones_heatmap_matrix2, scale = "none", cluster_rows = F, cluster_cols = F)

#ggsave(plot = pheatmap(cd8_topclones_heatmap_matrix2, scale = "none", cluster_rows = F, cluster_cols = F), "final_fig/heatmap_top20_ordered/cd8_tra_noscale.svg")

without 1st sequence

### without 1st sequence

cd8_topclones_heatmap_matrix2 <- as.matrix(cd8_topclones_heatmap_matrix[2:20,2:15])
rownames(cd8_topclones_heatmap_matrix2) <- cd8_topclones_heatmap_matrix$aaSeqCDR3[2:20]

cd8_topclones_heatmap_matrix2 <- cd8_topclones_heatmap_matrix2[match(cd8_topclones_20sequences_tra[2:20], rownames(cd8_topclones_heatmap_matrix2)),]

pheatmap(cd8_topclones_heatmap_matrix2, scale = "none", cluster_rows = F, cluster_cols = F)

#ggsave(plot = pheatmap(cd8_topclones_heatmap_matrix2, scale = "none", cluster_rows = F, cluster_cols = F), "final_fig/heatmap_top20_ordered/cd8_tra_noscale_without.svg")

TRB

CD4

# trb

cd4_topclones_heatmap_matrix <- prop.table.trb2 %>% filter(aaSeqCDR3 %in% cd4_topclones_20sequences_trb) %>% select(aaSeqCDR3, starts_with("CD4")) %>% select(1,14,15,9:13,7,8,2:6)
  
cd4_topclones_heatmap_matrix2 <- as.matrix(cd4_topclones_heatmap_matrix[,2:15])
rownames(cd4_topclones_heatmap_matrix2) <- cd4_topclones_heatmap_matrix$aaSeqCDR3

cd4_topclones_heatmap_matrix2 <- cd4_topclones_heatmap_matrix2[match( cd4_topclones_20sequences_trb, rownames(cd4_topclones_heatmap_matrix2)),]

pheatmap(cd4_topclones_heatmap_matrix2, scale = "none", cluster_rows = F, cluster_cols = F)

#ggsave(plot = pheatmap(cd4_topclones_heatmap_matrix2, scale = "none", cluster_rows = F, cluster_cols = F), "final_fig/heatmap_top20_ordered/cd4_trb_noscale.svg")

CD8

# trb

cd8_topclones_heatmap_matrix <- prop.table.trb2 %>% filter(aaSeqCDR3 %in% cd8_topclones_20sequences_trb) %>% select(aaSeqCDR3, starts_with("CD8")) %>% select(1,13:15,9:12,6:8,2:5)
  
cd8_topclones_heatmap_matrix2 <- as.matrix(cd8_topclones_heatmap_matrix[,2:15])
rownames(cd8_topclones_heatmap_matrix2) <- cd8_topclones_heatmap_matrix$aaSeqCDR3

cd8_topclones_heatmap_matrix2 <- cd8_topclones_heatmap_matrix2[match( cd8_topclones_20sequences_trb, rownames(cd8_topclones_heatmap_matrix2)),]

pheatmap(cd8_topclones_heatmap_matrix2, scale = "none", cluster_rows = F, cluster_cols = F)

# png("final_fig/heatmap_top20_ordered/cd8_trb_noscale.png", width = 400, height = 500)
# pheatmap(cd8_topclones_heatmap_matrix2, scale = "none", cluster_rows = F, cluster_cols = F)
# dev.off()

#ggsave(plot = pheatmap(cd8_topclones_heatmap_matrix2, scale = "none", cluster_rows = F, cluster_cols = F), "final_fig/heatmap_top20_ordered/cd8_trb_noscale.svg")

Repertoire overlap

binary_tra <- excel_count_table_tra %>% 
  mutate(nkt_trav11_traj18 = if_else(
    (grepl(allVHitsWithScore, pattern = "TRAV11") | 
    (grepl(allJHitsWithScore, pattern = "TRAJ18"))),"yes","no")) %>% 
  filter(nkt_trav11_traj18 == "no") %>% 
  mutate_at(vars(starts_with("CD")), .funs = binary) 

# count_overlap <- function(df, column_number){
#   repertoire <- df %>% select(vars(column_number), aaSeqCDR3) %>% filter(vars(column_number)>0) %>% pull("aaSeqCDR3")
#   nrow_column <- length(repertoire)
#   intersect_rept <- length(intersect(repertoire, most_diverse_repertoire))
#   pct <- intersect_rept/length()
#   return(x)
# }

binary_tra_longer <- binary_tra %>% select(starts_with("CD"), aaSeqCDR3) %>% pivot_longer(!aaSeqCDR3, names_to = "num_id")

df_all4 <- data.frame("")

for(j in levels(factor(binary_tra_longer$num_id))){
  subset1 <- binary_tra_longer %>% filter(num_id == j & value>0) %>% pull("aaSeqCDR3")
  vector_overlap <- c()
  for(i in levels(factor(binary_tra_longer$num_id))){
    subset2 <- binary_tra_longer %>% filter(num_id == i & value>0) %>% pull("aaSeqCDR3")
    intersect_rep <- length(intersect(subset1, subset2))
    total <- length(subset2)
    vector_overlap <- c(vector_overlap,intersect_rep/total)
  }
  df <- as.data.frame(x = vector_overlap)
  colnames(df) <- j
  df
  df_all4 <- cbind(df_all4, df)
}

df_all4 <- df_all4[,2:29]
rownames(df_all4) <- colnames(df_all4)

#write.csv(df_all4, "rep_overlap_tra.csv")
df24 <- df_all4
df24[df24 == 1] <- 0

### plot dotplot of overlaps

df25 <- df24 %>% rownames_to_column("id") %>%  pivot_longer(-id)

g2 <- ggplot(df25, aes(id, factor(name, levels = rev(levels(factor(name)))))) + 
  geom_point(aes(size = value*100, colour = value*100)) + 
  theme_bw() 

g2 + scale_size_continuous(range=c(7,12)) +
  geom_text(aes(label = round(value*100, digits = 1))) + 
  scale_colour_gradient2(low = "lightskyblue", mid = "lightsteelblue2", high = "salmon") +
  theme(axis.text.x = element_text(angle = 90)) 

Rozdelene na CD4 a CD8

rep_overlap_tra <- read_csv("rep_overlap_tra.csv") %>% column_to_rownames("...1")

rep_overlap_tra_cd4 <- rep_overlap_tra[1:14,1:14]
rep_overlap_tra_cd8 <- rep_overlap_tra[15:28,15:28]

rep_overlap_tra_cd4 <- rep_overlap_tra_cd4[ rev(c(5, 4, 3, 2, 1, 7, 6, 12, 11, 10, 9, 8, 14, 13)),
                                            rev(c(5, 4, 3, 2, 1, 7, 6, 12, 11, 10, 9, 8, 14, 13))]

levels_names <- colnames(rep_overlap_tra_cd4)

df_overlap_tra_cd4 <- rep_overlap_tra_cd4
df_overlap_tra_cd4[df_overlap_tra_cd4 == 1] <- 0

### plot dotplot of overlaps

df_overlap_tra_cd4 <- df_overlap_tra_cd4 %>% rownames_to_column("id") %>%  pivot_longer(-id)

g2 <- ggplot(df_overlap_tra_cd4, aes(x = factor(name, levels = levels_names), y = factor(id, levels = rev(levels_names)))) + 
  geom_point(aes(size = value*100, colour = value*100)) + 
  theme_bw() 

g2 + scale_size_continuous(range=c(5,9)) +
  geom_text(aes(label = round(value*100, digits = 1)), size = 3) + 
  scale_colour_gradient2(low = "white", mid = "lightsteelblue", high = "salmon") +
  theme(axis.text.x = element_text(angle = 90)) + ggtheme()

#ggsave(file = "./final_fig/overlap_cd4_tra.png", width = 20, height = 16, units = "cm")
#ggsave(file = "./final_fig/overlap_cd4_tra.svg", width = 20, height = 16, units = "cm")
rep_overlap_tra_cd8 <- rep_overlap_tra_cd8[rev(c(12:14,8:11,5:7,1:4)),rev(c(12:14,8:11,5:7,1:4))]

levels_names <- rev(colnames(rep_overlap_tra_cd8))

df_overlap_tra_cd8 <- rep_overlap_tra_cd8
df_overlap_tra_cd8[df_overlap_tra_cd8 == 1] <- 0

### plot dotplot of overlaps

df_overlap_tra_cd8 <- df_overlap_tra_cd8 %>% rownames_to_column("id") %>%  pivot_longer(-id)

g2 <- ggplot(df_overlap_tra_cd8, aes(factor(name, levels = levels_names), factor(id, levels = rev(levels_names)))) + 
  geom_point(aes(size = value*100, colour = value*100)) + 
  theme_bw() 

g2 + scale_size_continuous(range=c(5,9)) +
  geom_text(aes(label = round(value*100, digits = 1)), size = 3) + 
  scale_colour_gradient2(low = "white", mid = "lightsteelblue", high = "salmon") +
  theme(axis.text.x = element_text(angle = 90)) + ggtheme()

#ggsave(file = "./final_fig/overlap_cd8_tra.png", width = 20, height = 16, units = "cm")
#ggsave(file = "./final_fig/overlap_cd8_tra.svg", width = 20, height = 16, units = "cm")

A la violin

violin_tra_cd4 <- rep_overlap_tra_cd4 %>% 
  rownames_to_column("sample1") %>% 
  pivot_longer(!sample1, names_to = "sample2") %>% 
  mutate(sample2 = stringr::str_replace_all(sample2, pattern = "Lymph nodes", replacement = "LN"))  %>% 
  mutate(sample1 = stringr::str_replace_all(sample1, pattern = "Lymph nodes", replacement = "LN"))  %>% 
  separate(sample1, into = c("Cell_type1","Organ1","Strain1","Exp1"), remove = F)  %>% 
  separate(sample2, into = c("Cell_type2","Organ2","Strain2","Exp2"), remove = F) %>% 
  filter(value<1)


violin_tra_cd8 <- rep_overlap_tra_cd8 %>% 
  rownames_to_column("sample1") %>% 
  pivot_longer(!sample1, names_to = "sample2") %>% 
  mutate(sample2 = stringr::str_replace_all(sample2, pattern = "Lymph nodes", replacement = "LN"))  %>% 
  mutate(sample1 = stringr::str_replace_all(sample1, pattern = "Lymph nodes", replacement = "LN"))  %>% 
  separate(sample1, into = c("Cell_type1","Organ1","Strain1","Exp1"), remove = F)  %>% 
  separate(sample2, into = c("Cell_type2","Organ2","Strain2","Exp2"), remove = F) %>% 
  filter(value<1)
violin_tra_cd4 %>% filter(Organ1 == "Thymus" & Organ2 == "Thymus") %>% 
  mutate(organ_strain1 = paste(Organ1, Strain1), 
                          organ_strain2 = paste(Organ2, Strain2)) %>% 
  mutate(comparison = paste(organ_strain1, organ_strain2, sep = " - ")) %>% 
  filter(comparison %in% c("Thymus WT - Thymus WT", "Thymus CA - Thymus WT", "Thymus CAKR - Thymus WT")) %>% 
  ggplot(aes(x = comparison, y = value)) +
  geom_dotplot(binaxis='y', stackdir='center', dotsize=0) + 
  geom_jitter(position=position_jitter(0.2), size = 2, aes(color = comparison)) +
  ggtitle("CD4 TRA Thymus") +
  stat_summary(fun = "mean",
               geom = "crossbar", 
               width = 0.5,
               colour = "black") +
  ylab("% repertoire overlap") + 
  theme_classic() + 
  ggtheme() + 
  theme(axis.text.x = element_text(angle = 90)) + 
  scale_color_manual(values = c("dodgerblue","indianred2","gray40")) + 
  ylim(c(0,0.35)) + xlab("")

#ggsave("./final_fig/overlaps/rep_overlap_cd4_tra_thy.png", width = 9.5, height = 9.5,  units = "cm")
#ggsave("./final_fig/overlaps/rep_overlap_cd4_tra_thy.eps", width = 9.5, height = 9.5,  units = "cm")
violin_tra_cd4 %>% filter(Organ1 == "LN" & Organ2 == "LN") %>% 
  mutate(organ_strain1 = paste(Organ1, Strain1), 
                          organ_strain2 = paste(Organ2, Strain2)) %>% 
  mutate(comparison = paste(organ_strain1, organ_strain2, sep = " - ")) %>% 
  filter(comparison %in% c("LN WT - LN WT", "LN CA - LN WT", "LN CAKR - LN WT")) %>% 
  ggplot(aes(x = comparison, y = value)) +
  geom_dotplot(binaxis='y', stackdir='center', dotsize=0) + 
  geom_jitter(position=position_jitter(0.2), size = 2, aes(color = comparison)) +
  ggtitle("CD4 TRA LN") +
  stat_summary(fun = "mean",
               geom = "crossbar", 
               width = 0.5,
               colour = "black") +
  ylab("% repertoire overlap") + 
  theme_classic() + 
  ggtheme() + 
  theme(axis.text.x = element_text(angle = 90)) + 
  scale_color_manual(values = c("dodgerblue","indianred2","gray40")) + 
  ylim(c(0,0.35)) + xlab("")

#ggsave("./final_fig/overlaps/rep_overlap_cd4_tra_LN.png", width = 8, height = 8,  units = "cm")
#ggsave("./final_fig/overlaps/rep_overlap_cd4_tra_LN.eps", width = 8, height = 8,  units = "cm")
violin_tra_cd8 %>% filter(Organ1 == "Thymus" & Organ2 == "Thymus") %>% 
  mutate(organ_strain1 = paste(Organ1, Strain1), 
                          organ_strain2 = paste(Organ2, Strain2)) %>% 
  mutate(comparison = paste(organ_strain1, organ_strain2, sep = " - ")) %>% 
  filter(comparison %in% c("Thymus WT - Thymus WT", "Thymus CA - Thymus WT", "Thymus CAKR - Thymus WT")) %>% 
  ggplot(aes(x = comparison, y = value)) +
  geom_dotplot(binaxis='y', stackdir='center', dotsize=0) + 
  geom_jitter(position=position_jitter(0.2), size = 2, aes(color = comparison)) +
  ggtitle("CD8 TRA Thymus") +
  stat_summary(fun = "mean",
               geom = "crossbar", 
               width = 0.5,
               colour = "black") +
  ylab("% repertoire overlap") + 
  theme_classic() + 
  ggtheme() + 
  theme(axis.text.x = element_text(angle = 90)) + 
  scale_color_manual(values = c("dodgerblue","indianred2","gray40")) + 
  ylim(c(0,0.35)) + xlab("")

#ggsave("./final_fig/overlaps/rep_overlap_cd8_tra_thy.png", width = 9.5, height = 9.5,  units = "cm")
#ggsave("./final_fig/overlaps/rep_overlap_cd8_tra_thy.eps", width = 9.5, height = 9.5,  units = "cm")
violin_tra_cd8 %>% filter(Organ1 == "LN" & Organ2 == "LN") %>% 
  mutate(organ_strain1 = paste(Organ1, Strain1), 
                          organ_strain2 = paste(Organ2, Strain2)) %>% 
  mutate(comparison = paste(organ_strain1, organ_strain2, sep = " - ")) %>% 
  filter(comparison %in% c("LN WT - LN WT", "LN CA - LN WT", "LN CAKR - LN WT")) %>% 
  ggplot(aes(x = comparison, y = value)) +
  geom_dotplot(binaxis='y', stackdir='center', dotsize=0) + 
  geom_jitter(position=position_jitter(0.2), size = 2, aes(color = comparison)) +
  ggtitle("CD8 TRA LN") +
  stat_summary(fun = "mean",
               geom = "crossbar", 
               width = 0.5,
               colour = "black") +
  ylab("% repertoire overlap") + 
  theme_classic() + 
  ggtheme() + 
  theme(axis.text.x = element_text(angle = 90)) + 
  scale_color_manual(values = c("dodgerblue","indianred2","gray40")) + 
  ylim(c(0,0.35)) + xlab("")

#ggsave("./final_fig/overlaps/rep_overlap_cd8_tra_LN.png", width = 8, height = 8, units = "cm")
#ggsave("./final_fig/overlaps/rep_overlap_cd8_tra_LN.eps", width = 8, height = 8, units = "cm")

TRB

Vsechny vzorky se vsema

binary_trb <- excel_count_table_trb %>% 
  mutate_at(vars(starts_with("CD")), .funs = binary) 

binary_trb_longer <- binary_trb %>% select(starts_with("CD"), aaSeqCDR3) %>% pivot_longer(!aaSeqCDR3, names_to = "num_id")

df_all4 <- data.frame("")

for(j in levels(factor(binary_trb_longer$num_id))){
  subset1 <- binary_trb_longer %>% filter(num_id == j & value>0) %>% pull("aaSeqCDR3")
  vector_overlap <- c()
  for(i in levels(factor(binary_trb_longer$num_id))){
    subset2 <- binary_trb_longer %>% filter(num_id == i & value>0) %>% pull("aaSeqCDR3")
    intersect_rep <- length(intersect(subset1, subset2))
    total <- length(subset2)
    vector_overlap <- c(vector_overlap,intersect_rep/total)
  }
  df <- as.data.frame(x = vector_overlap)
  colnames(df) <- j
  df
  df_all4 <- cbind(df_all4, df)
}

df_all4 <- df_all4[,2:29]
rownames(df_all4) <- colnames(df_all4)

#write.csv(df_all4, "rep_overlap_trb.csv")
df24 <- df_all4
df24[df24 == 1] <- 0

### plot dotplot of overlaps

df25 <- df24 %>% rownames_to_column("id") %>%  pivot_longer(-id)

g2 <- ggplot(df25, aes(id, factor(name, levels = rev(levels(factor(name)))))) + 
  geom_point(aes(size = value*100, colour = value*100)) + 
  theme_bw() 

g2 + scale_size_continuous(range=c(7,12)) +
  geom_text(aes(label = round(value*100, digits = 1))) + 
  scale_colour_gradient2(low = "lightskyblue", mid = "lightsteelblue2", high = "salmon") +
  theme(axis.text.x = element_text(angle = 90)) 

Rozdelene na CD4 a CD8

rep_overlap_trb <- read_csv("rep_overlap_trb.csv") %>% column_to_rownames("...1")

rep_overlap_trb_cd4 <- rep_overlap_trb[1:14,1:14]
rep_overlap_trb_cd8 <- rep_overlap_trb[15:28,15:28]

rep_overlap_trb_cd4 <- rep_overlap_trb_cd4[rev(c(13,14,8:12,6,7,1:5)),rev(c(13,14,8:12,6,7,1:5))]

levels_names <- rev(colnames(rep_overlap_trb_cd4))


df_overlap_trb_cd4 <- rep_overlap_trb_cd4
df_overlap_trb_cd4[df_overlap_trb_cd4 == 1] <- 0

### plot dotplot of overlaps

df_overlap_trb_cd4 <- df_overlap_trb_cd4 %>% rownames_to_column("id") %>%  pivot_longer(-id)

g2 <- ggplot(df_overlap_trb_cd4, aes(factor(name, levels = levels_names), factor(id, levels = rev(levels_names)))) + 
  geom_point(aes(size = value*100, colour = value*100)) + 
  theme_bw() 

g2 + scale_size_continuous(range=c(5,9)) +
  geom_text(aes(label = round(value*100, digits = 1)), size = 3) + 
  scale_colour_gradient2(low = "white", mid = "lightsteelblue", high = "salmon") +
  theme(axis.text.x = element_text(angle = 90)) + ggtheme()

#ggsave(file = "./final_fig/overlap_cd4_trb.png", width = 20, height = 16, units = "cm")
#ggsave(file = "./final_fig/overlap_cd4_trb.svg", width = 20, height = 16, units = "cm")
rep_overlap_trb_cd8 <- rep_overlap_trb_cd8[rev(c(12:14,8:11,5:7,1:4)),rev(c(12:14,8:11,5:7,1:4))]

levels_names <- rev(colnames(rep_overlap_trb_cd8))

df_overlap_trb_cd8 <- rep_overlap_trb_cd8
df_overlap_trb_cd8[df_overlap_trb_cd8 == 1] <- 0

### plot dotplot of overlaps

df_overlap_trb_cd8 <- df_overlap_trb_cd8 %>% rownames_to_column("id") %>%  pivot_longer(-id)

g2 <- ggplot(df_overlap_trb_cd8, aes(factor(name, levels = levels_names), factor(id, levels = rev(levels_names)))) + 
  geom_point(aes(size = value*100, colour = value*100)) + 
  theme_bw() 

g2 + scale_size_continuous(range=c(5,9)) +
  geom_text(aes(label = round(value*100, digits = 1)), size = 3) + 
  scale_colour_gradient2(low = "white", mid = "lightsteelblue", high = "salmon") +
  theme(axis.text.x = element_text(angle = 90)) + ggtheme()

#ggsave(file = "./final_fig/overlap_cd8_trb.png", width = 20, height = 16, units = "cm")
#ggsave(file = "./final_fig/overlap_cd8_trb.svg", width = 20, height = 16, units = "cm")

A la violin

violin_trb_cd4 <- rep_overlap_trb_cd4 %>% 
  rownames_to_column("sample1") %>% 
  pivot_longer(!sample1, names_to = "sample2") %>% 
  mutate(sample2 = stringr::str_replace_all(sample2, pattern = "Lymph nodes", replacement = "LN"))  %>% 
  mutate(sample1 = stringr::str_replace_all(sample1, pattern = "Lymph nodes", replacement = "LN"))  %>% 
  separate(sample1, into = c("Cell_type1","Organ1","Strain1","Exp1"), remove = F)  %>% 
  separate(sample2, into = c("Cell_type2","Organ2","Strain2","Exp2"), remove = F) %>% 
  filter(value<1)

violin_trb_cd8 <- rep_overlap_trb_cd8 %>% 
  rownames_to_column("sample1") %>% 
  pivot_longer(!sample1, names_to = "sample2") %>% 
  mutate(sample2 = stringr::str_replace_all(sample2, pattern = "Lymph nodes", replacement = "LN"))  %>% 
  mutate(sample1 = stringr::str_replace_all(sample1, pattern = "Lymph nodes", replacement = "LN"))  %>% 
  separate(sample1, into = c("Cell_type1","Organ1","Strain1","Exp1"), remove = F)  %>% 
  separate(sample2, into = c("Cell_type2","Organ2","Strain2","Exp2"), remove = F) %>% 
  filter(value<1)
violin_trb_cd8 %>% filter(Organ1 == "Thymus" & Organ2 == "Thymus") %>% 
  mutate(organ_strbin1 = paste(Organ1, Strain1), 
                          organ_strbin2 = paste(Organ2, Strain2)) %>% 
  mutate(comparison = paste(organ_strbin1, organ_strbin2, sep = " - ")) %>% 
  filter(comparison %in% c("Thymus WT - Thymus WT", "Thymus CA - Thymus WT", "Thymus CAKR - Thymus WT")) %>% 
  ggplot(aes(x = comparison, y = value)) +
  geom_dotplot(binaxis='y', stackdir='center', dotsize=0) + 
  geom_jitter(position=position_jitter(0.2), size = 2, aes(color = comparison)) +
  ggtitle("CD8 TRB Thymus") +
  stat_summary(fun = "mean",
               geom = "crossbar", 
               width = 0.5,
               colour = "black") +
  ylab("% repertoire overlap") + 
  theme_classic() + 
  ggtheme() + 
  theme(axis.text.x = element_text(angle = 90)) + 
  scale_color_manual(values = c("dodgerblue","indianred2","gray40")) + 
  ylim(c(0,0.25)) + xlab("")

#ggsave("./final_fig/overlaps/rep_overlap_cd8_trb_thy.png", width = 9.5, height = 9.5,  units = "cm")
#ggsave("./final_fig/overlaps/rep_overlap_cd8_trb_thy.eps", width = 9.5, height = 9.5,  units = "cm")
violin_trb_cd8 %>% filter(Organ1 == "LN" & Organ2 == "LN") %>% 
  mutate(organ_strbin1 = paste(Organ1, Strain1), 
                          organ_strbin2 = paste(Organ2, Strain2)) %>% 
  mutate(comparison = paste(organ_strbin1, organ_strbin2, sep = " - ")) %>% 
  filter(comparison %in% c("LN WT - LN WT", "LN CA - LN WT", "LN CAKR - LN WT")) %>% 
  ggplot(aes(x = comparison, y = value)) +
  geom_dotplot(binaxis='y', stackdir='center', dotsize=0) + 
  geom_jitter(position=position_jitter(0.2), size = 2, aes(color = comparison)) +
  ggtitle("CD8 TRB LN") +
  stat_summary(fun = "mean",
               geom = "crossbar", 
               width = 0.5,
               colour = "black") +
  ylab("% repertoire overlap") + 
  theme_classic() + 
  ggtheme() + 
  theme(axis.text.x = element_text(angle = 90)) + 
  scale_color_manual(values = c("dodgerblue","indianred2","gray40")) + 
  ylim(c(0,0.25)) + xlab("")

#ggsave("./final_fig/overlaps/rep_overlap_cd8_trb_LN.png", width = 8, height = 8,  units = "cm")
#ggsave("./final_fig/overlaps/rep_overlap_cd8_trb_LN.eps", width = 8, height = 8,  units = "cm")
violin_trb_cd4 %>% filter(Organ1 == "Thymus" & Organ2 == "Thymus") %>% 
  mutate(organ_strbin1 = paste(Organ1, Strain1), 
                          organ_strbin2 = paste(Organ2, Strain2)) %>% 
  mutate(comparison = paste(organ_strbin1, organ_strbin2, sep = " - ")) %>% 
  filter(comparison %in% c("Thymus WT - Thymus WT", "Thymus CA - Thymus WT", "Thymus CAKR - Thymus WT")) %>% 
  ggplot(aes(x = comparison, y = value)) +
  geom_dotplot(binaxis='y', stackdir='center', dotsize=0) + 
  geom_jitter(position=position_jitter(0.2), size = 2, aes(color = comparison)) +
  ggtitle("CD4 TRB Thymus") +
  stat_summary(fun = "mean",
               geom = "crossbar", 
               width = 0.5,
               colour = "black") +
  ylab("% repertoire overlap") + 
  theme_classic() + 
  ggtheme() + 
  theme(axis.text.x = element_text(angle = 90)) + 
  scale_color_manual(values = c("dodgerblue","indianred2","gray40")) + 
  ylim(c(0,0.20)) + xlab("")

#ggsave("./final_fig/overlaps/rep_overlap_cd4_trb_thy.png", width = 9.5, height = 9.5,  units = "cm")
#ggsave("./final_fig/overlaps/rep_overlap_cd4_trb_thy.eps", width = 9.5, height = 9.5,  units = "cm")
violin_trb_cd4 %>% filter(Organ1 == "LN" & Organ2 == "LN") %>% 
  mutate(organ_strbin1 = paste(Organ1, Strain1), 
                          organ_strbin2 = paste(Organ2, Strain2)) %>% 
  mutate(comparison = paste(organ_strbin1, organ_strbin2, sep = " - ")) %>% 
  filter(comparison %in% c("LN WT - LN WT", "LN CA - LN WT", "LN CAKR - LN WT")) %>% 
  ggplot(aes(x = comparison, y = value)) +
  geom_dotplot(binaxis='y', stackdir='center', dotsize=0) + 
  geom_jitter(position=position_jitter(0.2), size = 2, aes(color = comparison)) +
  ggtitle("CD4 TRB LN") +
  stat_summary(fun = "mean",
               geom = "crossbar", 
               width = 0.5,
               colour = "black") +
  ylab("% repertoire overlap") + 
  theme_classic() + 
  ggtheme() + 
  theme(axis.text.x = element_text(angle = 90)) + 
  scale_color_manual(values = c("dodgerblue","indianred2","gray40")) + 
  ylim(c(0,0.20)) + xlab("")

#ggsave("./final_fig/overlaps/rep_overlap_cd4_trb_LN.png", width = 8, height = 8,  units = "cm")
#ggsave("./final_fig/overlaps/rep_overlap_cd4_trb_LN.eps", width = 8, height = 8,  units = "cm")

SessionInfo

sessionInfo()
## R version 4.2.1 (2022-06-23)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.5 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] SmartEDA_0.3.8    kableExtra_1.3.4  factoextra_1.0.7  pheatmap_1.0.12  
##  [5] immunarch_0.6.9   data.table_1.14.2 dtplyr_1.2.2      patchwork_1.1.2  
##  [9] cowplot_1.1.1     readxl_1.4.1      forcats_0.5.2     stringr_1.4.1    
## [13] dplyr_1.0.9       purrr_0.3.4       readr_2.1.2       tidyr_1.2.0      
## [17] tibble_3.1.8      ggplot2_3.3.6     tidyverse_1.3.2  
## 
## loaded via a namespace (and not attached):
##   [1] uuid_1.1-0          backports_1.4.1     circlize_0.4.15    
##   [4] fastmatch_1.1-3     systemfonts_1.0.4   plyr_1.8.7         
##   [7] igraph_1.3.4        digest_0.6.29       foreach_1.5.2      
##  [10] htmltools_0.5.3     ggalluvial_0.12.3   fansi_1.0.3        
##  [13] magrittr_2.0.3      googlesheets4_1.0.1 cluster_2.1.4      
##  [16] doParallel_1.0.17   tzdb_0.3.0          modelr_0.1.9       
##  [19] vroom_1.5.7         svglite_2.1.0       lpSolve_5.6.15     
##  [22] rmdformats_1.0.4    colorspace_2.0-3    rvest_1.0.3        
##  [25] ggrepel_0.9.1       haven_2.5.1         xfun_0.31          
##  [28] crayon_1.5.1        jsonlite_1.8.0      phangorn_2.9.0     
##  [31] iterators_1.0.14    ape_5.6-2           glue_1.6.2         
##  [34] gtable_0.3.0        gargle_1.2.0        webshot_0.5.3      
##  [37] UpSetR_1.4.0        car_3.1-0           kernlab_0.9-31     
##  [40] shape_1.4.6         prabclus_2.3-2      DEoptimR_1.0-11    
##  [43] ISLR_1.4            abind_1.4-5         scales_1.2.1       
##  [46] DBI_1.1.3           GGally_2.1.2        rstatix_0.7.0      
##  [49] Rcpp_1.0.9          viridisLite_0.4.1   xtable_1.8-4       
##  [52] bit_4.0.4           mclust_5.4.10       stats4_4.2.1       
##  [55] sampling_2.9        httr_1.4.4          RColorBrewer_1.1-3 
##  [58] fpc_2.2-9           modeltools_0.2-23   ellipsis_0.3.2     
##  [61] farver_2.1.1        reshape_0.8.9       pkgconfig_2.0.3    
##  [64] flexmix_2.3-18      nnet_7.3-17         sass_0.4.2         
##  [67] ggseqlogo_0.1       dbplyr_2.2.1        utf8_1.2.2         
##  [70] labeling_0.4.2      tidyselect_1.1.2    rlang_1.0.4        
##  [73] reshape2_1.4.4      later_1.3.0         munsell_0.5.0      
##  [76] cellranger_1.1.0    tools_4.2.1         cachem_1.0.6       
##  [79] cli_3.3.0           generics_0.1.3      broom_1.0.0        
##  [82] evaluate_0.16       fastmap_1.1.0       yaml_2.3.5         
##  [85] bit64_4.0.5         knitr_1.39          fs_1.5.2           
##  [88] robustbase_0.95-0   nlme_3.1-159        mime_0.12          
##  [91] xml2_1.3.3          compiler_4.2.1      shinythemes_1.2.0  
##  [94] rstudioapi_0.14     ggsignif_0.6.3      reprex_2.0.2       
##  [97] bslib_0.4.0         stringi_1.7.8       highr_0.9          
## [100] lattice_0.20-45     Matrix_1.4-1        vctrs_0.4.1        
## [103] stringdist_0.9.8    pillar_1.8.1        lifecycle_1.0.1    
## [106] jquerylib_0.1.4     GlobalOptions_0.1.2 httpuv_1.6.5       
## [109] R6_2.5.1            bookdown_0.27       promises_1.2.0.1   
## [112] gridExtra_2.3       codetools_0.2-18    MASS_7.3-58.1      
## [115] assertthat_0.2.1    withr_2.5.0         rlist_0.4.6.2      
## [118] diptest_0.76-0      parallel_4.2.1      hms_1.1.2          
## [121] quadprog_1.5-8      grid_4.2.1          class_7.3-20       
## [124] rmarkdown_2.14      carData_3.0-5       googledrive_2.0.0  
## [127] ggpubr_0.4.0        shiny_1.7.2         lubridate_1.8.0